#import
import os.path
from gensim import corpora
from gensim.models import LsiModel
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from gensim.models.coherencemodel import CoherenceModel
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import sklearn as s
import dataframe_image as dfi
import networkx as nx
import mantel
C:\ProgramData\Anaconda3\lib\site-packages\gensim\similarities\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning. warnings.warn(msg)
def load_data(path,file_name,seperator):
"""
Input : path and file_name
Purpose: loading text file
Output : list of paragraphs/documents and
title(initial 100 words considred as title of document)
"""
documents_list = []
titles=[]
document = ''
i=0
with open( os.path.join(path, file_name) ,"r", encoding="utf8") as fin:
data = fin.read()
words = data.split()
fin.seek(0)
print('Number of words in text file :', len(words))
for line in fin.readlines():
text = line.strip()
i=i+1
if text !='':
document = document+text
if text == seperator:
documents_list.append(document)
document = ''
documents_list.append(document)
print("Total Number of Documents:",len(documents_list))
titles.append( text[0:min(len(text),100)] )
return documents_list,titles,len(words)
document_list,titles,word_count_science=load_data("","covid-science-15.txt",'NEW_PAPER')
Number of words in text file : 88023 Total Number of Documents: 15
def preprocess_data(doc_set):
"""
Input : docuemnt list
Purpose: preprocess text (tokenize, removing stopwords, and stemming)
Output : preprocessed text
"""
# initialize regex tokenizer
tokenizer = RegexpTokenizer(r'\w+')
# create English stop words list
en_stop = set(stopwords.words('english'))
# Create p_stemmer of class PorterStemmer
p_stemmer = PorterStemmer()
# list for tokenized documents in loop
texts = []
# loop through document list
for i in doc_set:
# clean and tokenize document string
raw = i.lower()
tokens = tokenizer.tokenize(raw)
tokens = [i for i in tokens if len(i)>4]
print(tokens)
# remove stop words from tokens
stopped_tokens = [i for i in tokens if not i in en_stop]
# stem tokens
stemmed_tokens = [p_stemmer.stem(i) for i in stopped_tokens]
# add tokens to list
texts.append(stemmed_tokens)
return texts
clean_text=preprocess_data(document_list)
['covidgr', 'dataset', 'covid', 'sdnetmethodology', 'predicting', 'covid', '19based', 'chest', 'imagess', 'tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herreraabstract', 'currently', 'coronavirus', 'disease', 'covid', 'infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans', 'orchest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'mostmedical', 'centers', 'hence', 'cases', 'imagesbecome', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networkshave', 'great', 'potential', 'building', 'covid', 'triage', 'systemsand', 'detecting', 'covid', 'patients', 'especially', 'patients', 'withlow', 'severity', 'unfortunately', 'current', 'databases', 'allowbuilding', 'systems', 'highly', 'heterogeneousand', 'biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved', 'bymost', 'recent', 'covid', 'classification', 'models', 'under', 'aclose', 'collaboration', 'hospital', 'universitario', 'clínico', 'sancecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levelsmanuscript', 'received', 'september', 'revised', 'october', 'accepted', 'november', 'publication', 'november', 'current', 'version', 'december', 'supported', 'bythe', 'project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundaciónbbva', 'equipos', 'investigación', 'científica', 'covid', '192020', 'spanish', 'ministry', 'science', 'technology', 'under', 'theproject', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajalprogramme', '18136', 'gómez', 'supported', 'thefpu', 'programme', 'fpu16', '04765', 'charte', 'supported', 'fpuprogramme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'researchcouncil', 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def prepare_corpus(doc_clean):
"""
Input : clean document
Purpose: create term dictionary of our courpus and Converting list of documents (corpus) into Document Term Matrix
Output : term dictionary and Document Term Matrix
"""
# Creating the term dictionary of our courpus, where every unique term is assigned an index. dictionary = corpora.Dictionary(doc_clean)
dictionary = corpora.Dictionary(doc_clean)
# Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.
doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]
# generate LDA model
return dictionary,doc_term_matrix
def create_gensim_lsa_model(doc_clean,number_of_topics,words):
"""
Input : clean document, number of topics and number of words associated with each topic
Purpose: create LSA model using gensim
Output : return LSA model
"""
dictionary,doc_term_matrix=prepare_corpus(doc_clean)
# generate LSA model
lsamodel = LsiModel(doc_term_matrix, num_topics=number_of_topics, id2word = dictionary) # train model
#print(lsamodel.print_topics(num_topics=number_of_topics, num_words=words))
return lsamodel
# LSA Model
number_of_topics=15
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values
#These variables are for further experimentation in vertical comparission mantel test
model_science_articles = create_gensim_lsa_model(clean_text,number_of_topics,words)
dict(model_science.show_topic(0, topn=5))
{'covid': 0.48404637328080685,
'imag': 0.34556353402865697,
'model': 0.29152531367714396,
'dataset': 0.1920788517016384,
'class': 0.16833626183973904}
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph21.png')
# LSA Model
a=pd.DataFrame()
for i in range(number_of_topics):
words_science =dict(model_science.show_topic(i, topn=words))
b=pd.DataFrame(words_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_science.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled24.png')
cosine-similarity
covid imag model dataset class
covid 1 0.117954 0.182831 -0.0841955 0.510676
imag 0.117954 1 -0.124147 0.320962 0.0668724
model 0.182831 -0.124147 1 -0.0262809 0.0105534
dataset -0.0841955 0.320962 -0.0262809 1 0.0909586
class 0.510676 0.0668724 0.0105534 0.0909586 1
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig24.png')
document_list,titles,word_count_psy=load_data("","covid-psychology-15.txt",'NEW PAPER')
Number of words in text file : 58236 Total Number of Documents: 15
clean_text=preprocess_data(document_list)
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'thatpeople', 'compete', 'coordinate', 'evacuations', 'positionedpsychologically', 'individuals', 'rather', 'group', 'members', 'mintz', 'shows', 'evacuating', 'crowd', 'blocks', 'explained', 'terms', 'theprevalence', 'individual', 'competition', 'collective', 'setting', 'rather', 'terms', 'ofexcessive', 'emotion', 'these', 'cases', 'emergency', 'badly', 'absence', 'ofcollective', 'psychology', 'coordination', 'cooperation', 'cooperating', 'giving', 'support', 'carry', 'risks', 'which', 'acknowledged', 'evacuations', 'larger', 'group', 'slower', 'egress', 'because', 'speed', 'reducedthrough', 'people', 'interacting', 'other', 'aguirre', 'motivation', 'togive', 'support', 'other', 'survivors', 'risks', 'their', 'personal', 'safety', 'collective', 'emergency', 'always', 'particular', 'individuals', 'drury', 'cocking', 'reicher', 'covid', 'individualfrom', 'supporting', 'group', 'clear', 'where', 'supportive', 'behaviour', 'involves', 'physicalproximity', 'whether', 'delivering', 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'possible', 'threat', 'isseen', 'conveying', 'information', 'dependent', 'relevance', 'these', 'others', 'aparticular', 'context', 'which', 'often', 'function', 'shared', 'identity', 'based', 'weknow', 'about', 'social', 'influence', 'processes', 'other', 'contexts', 'bruder', 'caseof', 'covid', 'plausible', 'suppose', 'sight', 'others', 'community', 'routinelyobserving', 'ignoring', 'social', 'distancing', 'regulations', 'example', 'likely', 'strongsignal', 'around', 'safety', 'doing', 'particularly', 'where', 'identify', 'thecommunity', 'these', 'exemplars', 'prototypes', 'psychological', 'factors', 'interact', 'management', 'failures', 'explain', 'someemergency', 'events', 'badly', 'fearing', 'public', 'panic', 'leads', 'authorities', 'withholdinformation', 'about', 'emergency', 'drury', 'information', 'anemergency', 'increases', 'public', 'anxiety', 'public', 'perceives', 'information', 'isbeing', 'withheld', 'damages', 'their', 'relationship', 'authority', 'carteret', 'consequently', 'authorities', 'release', 'correct', 'information', 'thepublic', 'mistrust', 'covid', 'treat', 'thepublic', 'respect', 'order', 'build', 'trust', 'advice', 'given', 'behaviouralscientists', 'government', '24discussion', 'conclusionswe', 'emergencies', 'people', 'behave', 'selfishly', 'thoughtlessly', 'orthat', 'react', 'indeed', 'explained', 'research', 'suggests', 'conditionsfor', 'competition', 'prevail', 'cooperation', 'questioning', 'notionthat', 'public', 'reactions', 'default', 'major', 'cause', 'problems', 'covid', '19crisis', 'existing', 'literature', 'disasters', 'support', 'prima', 'faciemajor', 'problems', 'covid', 'response', 'outcomes', 'better', 'understoodotherwise', 'terms', 'political', 'under', 'reaction', 'systemic', 'issues', 'mismanagement', 'collective', 'panic', 'referred', 'disaster', 'literature', 'disasters', 'druryet', 'rather', 'neutral', 'description', 'people', 'actually', 'behave', 'bestunderstood', 'particular', 'discourse', 'cultural', 'representation', 'whichpsychologizes', 'indeed', 'pathologizes', 'public', 'responses', 'emergencies', 'disasters', 'given', 'known', 'about', 'under', 'reaction', 'systemic', 'factors', 'mismanagement', 'inemergencies', 'emphasize', 'instead', 'collective', 'behaviour', 'clearideological', 'functions', 'naturalizing', 'fatalities', 'distracts', 'causes', 'thusfrom', 'might', 'responsible', 'mismanagement', 'instead', 'blaming', 'victims', 'irony', 'course', 'being', 'problem', 'collective', 'psychology', 'inemergencies', 'solidarity', 'cooperation', 'commonly', 'witnessed', 'among', 'communitymembers', 'strangers', 'usually', 'solution', 'collective', 'psychology', 'therefore', 'andshould', 'harnessed', 'effectively', 'policy', 'practice', 'covid', 'response', 'elcheroth', 'drury', 'through', 'framing', 'threat', 'solution', 'collectiveterms', 'through', 'emphasizing', 'shared', 'norms', 'around', 'collective', 'being', 'safety', 'drury', 'people', 'emergencies', 'probably', 'because', 'ofcollective', 'psychology']
# LSA Model
number_of_topics=15
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))#dict and encoding matrix values
#These variables are for further experimentation in vertical comparission mantel test
model_psy_articles=create_gensim_lsa_model(clean_text,number_of_topics,words)
dict(model_psy.show_topic(0,topn=5))
{'covid': 0.4241833010171727,
'psycholog': 0.276494658493425,
'group': 0.25392735028600955,
'pandem': 0.22421308419475472,
'health': 0.2080533594503455}
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph22.png')
# LSA Model
a=pd.DataFrame()
for i in range(number_of_topics):
words_psy =dict(model_psy.show_topic(i, topn=words))
b=pd.DataFrame(words_psy,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_psy.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled25.png')
cosine-similarity
covid psycholog group pandem health
covid 1 0.235744 0.032874 0.48363 0.12375
psycholog 0.235744 1 0.00600425 0.355838 0.359423
group 0.032874 0.00600425 1 -0.0361535 -0.0297868
pandem 0.48363 0.355838 -0.0361535 1 0.466249
health 0.12375 0.359423 -0.0297868 0.466249 1
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig25.png')
def mantel_test(n):
words_science = dict(model_science.show_topic(0, topn=word_count_science))
words_psy = dict(model_psy.show_topic(0, topn=word_count_psy))
top_words_science = dict(model_science.show_topic(0, topn=n))
top_words_psy = dict(model_psy.show_topic(0,topn=n))
c={} #law words in non law
for key in top_words_science:
try:
c[key] = words_psy[key]
except KeyError:
c[key] = 0
top_words_psy.update(c)
b={} #non law words in law
for key in top_words_psy:
try:
b[key] = words_science[key]
except KeyError:
b[key] = 0
b.update(top_words_science)
top_words_science=b
#pairwise distance for law
a=pd.DataFrame()
for i in range(number_of_topics):
words_from_science =dict(model_science.show_topic(i, topn=word_count_science))
b=pd.DataFrame(words_from_science,index=[i])
a=a.append(b)
a=a.transpose()
df = pd.DataFrame(columns=list(top_words_science.keys()),
index = list(top_words_science.keys()))
l = list(top_words_science.keys())
print('Pairwise-distance')
for i in l:
for j in l:
try:
matrix=np.array([a.loc[i],a.loc[j]])
except KeyError:
matrix=np.zeros((2,3))
n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
df.loc[[i],[j]] = n[0,1]
print(df)
df1 = df.to_numpy()
#pairwise distace for non-law
a=pd.DataFrame()
for i in range(number_of_topics):
words_from_psy =dict(model_psy.show_topic(i, topn=word_count_psy))
b=pd.DataFrame(words_from_psy,index=[i])
a=a.append(b)
a=a.transpose()
df = pd.DataFrame(columns=list(top_words_psy.keys()),
index = list(top_words_psy.keys()))
l = list(top_words_psy.keys())
print('Pairwise-distance')
for i in l:
for j in l:
try:
matrix=np.array([a.loc[i],a.loc[j]])
except KeyError:
matrix=np.zeros((2,3))
n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
df.loc[[i],[j]] = n[0,1]
print(df)
df2 = df.to_numpy()
dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])
dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])
return mantel.test(dist1, dist2, perms=10000, method='pearson', tail='upper')
mantel_test(5)
Pairwise-distance
covid psycholog group pandem health imag \
covid 0 0.627345 0.616418 0.634626 0.599659 0.906064
psycholog 0.627345 1.16415e-10 0.066445 0.196729 0.149188 0.731796
group 0.616418 0.066445 0 0.200285 0.161369 0.717135
pandem 0.634626 0.196729 0.200285 0 0.198709 0.785046
health 0.599659 0.149188 0.161369 0.198709 0 0.762977
imag 0.906064 0.731796 0.717135 0.785046 0.762977 0
model 0.83963 0.684718 0.7009 0.68419 0.696724 1.06205
dataset 0.901475 0.596784 0.616381 0.636306 0.612924 0.781713
class 0.560897 0.47378 0.504815 0.554316 0.498453 0.844705
model dataset class
covid 0.83963 0.901475 0.560897
psycholog 0.684718 0.596784 0.47378
group 0.7009 0.616381 0.504815
pandem 0.68419 0.636306 0.554316
health 0.696724 0.612924 0.498453
imag 1.06205 0.781713 0.844705
model 0 0.919469 0.828103
dataset 0.919469 0 0.727202
class 0.828103 0.727202 0
Pairwise-distance
covid psycholog group pandem health imag \
covid 0 0.665039 0.949824 0.512287 0.798946 0.569647
psycholog 0.665039 0 0.916289 0.500813 0.645733 0.505794
group 0.949824 0.916289 1.49012e-08 0.869534 1.01224 0.778182
pandem 0.512287 0.500813 0.869534 0 0.561819 0.365353
health 0.798946 0.645733 1.01224 0.561819 0 0.627794
imag 0.569647 0.505794 0.778182 0.365353 0.627794 6.58545e-10
model 0.545144 0.519223 0.800488 0.386536 0.641427 0.13642
dataset 0.574902 0.496188 0.774211 0.371155 0.62841 0.0360889
class 0.577615 0.497861 0.771704 0.373614 0.631885 0.0460422
model dataset class
covid 0.545144 0.574902 0.577615
psycholog 0.519223 0.496188 0.497861
group 0.800488 0.774211 0.771704
pandem 0.386536 0.371155 0.373614
health 0.641427 0.62841 0.631885
imag 0.13642 0.0360889 0.0460422
model 0 0.122277 0.134249
dataset 0.122277 2.85158e-10 0.0254673
class 0.134249 0.0254673 0
(-0.5583274383531106, 0.9722, -1.7668867248811133)
mantel_test(10)
Pairwise-distance
covid psycholog group pandem health peopl \
covid 0 0.627345 0.616418 0.634626 0.599659 0.661675
psycholog 0.627345 1.16415e-10 0.066445 0.196729 0.149188 0.293753
group 0.616418 0.066445 0 0.200285 0.161369 0.298513
pandem 0.634626 0.196729 0.200285 0 0.198709 0.326716
health 0.599659 0.149188 0.161369 0.198709 0 0.330524
peopl 0.661675 0.293753 0.298513 0.326716 0.330524 7.45058e-09
impact 0.61597 0.0638 0.0972342 0.222891 0.142905 0.289812
social 0.737109 0.430898 0.420895 0.40153 0.430305 0.394205
studi 0.586486 0.164695 0.161258 0.247848 0.187599 0.313501
research 0.595436 0.0901519 0.120434 0.201167 0.0975713 0.322663
imag 0.906064 0.731796 0.717135 0.785046 0.762977 0.835098
model 0.83963 0.684718 0.7009 0.68419 0.696724 0.758934
dataset 0.901475 0.596784 0.616381 0.636306 0.612924 0.655583
class 0.560897 0.47378 0.504815 0.554316 0.498453 0.578072
perform 0.652411 0.356799 0.355504 0.423214 0.38486 0.449317
learn 0.735845 0.516397 0.514441 0.524195 0.497694 0.623015
detect 0.627337 0.325566 0.337485 0.425285 0.389054 0.432081
use 0.532273 0.223613 0.195537 0.314649 0.272496 0.344397
base 0.505415 0.210683 0.218844 0.264391 0.282322 0.372698
impact social studi research imag model \
covid 0.61597 0.737109 0.586486 0.595436 0.906064 0.83963
psycholog 0.0638 0.430898 0.164695 0.0901519 0.731796 0.684718
group 0.0972342 0.420895 0.161258 0.120434 0.717135 0.7009
pandem 0.222891 0.40153 0.247848 0.201167 0.785046 0.68419
health 0.142905 0.430305 0.187599 0.0975713 0.762977 0.696724
peopl 0.289812 0.394205 0.313501 0.322663 0.835098 0.758934
impact 1.31709e-09 0.443311 0.166424 0.0962653 0.736111 0.693722
social 0.443311 0 0.384823 0.452931 0.896969 0.817203
studi 0.166424 0.384823 0 0.192156 0.715145 0.69514
research 0.0962653 0.452931 0.192156 0 0.731434 0.691281
imag 0.736111 0.896969 0.715145 0.731434 0 1.06205
model 0.693722 0.817203 0.69514 0.691281 1.06205 0
dataset 0.590031 0.690754 0.579846 0.595966 0.781713 0.919469
class 0.461026 0.672743 0.476091 0.468226 0.844705 0.828103
perform 0.377568 0.586466 0.34259 0.372694 0.812935 0.551424
learn 0.482763 0.703538 0.471397 0.522492 0.905108 0.815302
detect 0.328156 0.569479 0.386567 0.323053 0.681579 0.653473
use 0.222522 0.510654 0.243858 0.243918 0.732506 0.728691
base 0.224732 0.493317 0.255434 0.218313 0.723226 0.663673
dataset class perform learn detect use \
covid 0.901475 0.560897 0.652411 0.735845 0.627337 0.532273
psycholog 0.596784 0.47378 0.356799 0.516397 0.325566 0.223613
group 0.616381 0.504815 0.355504 0.514441 0.337485 0.195537
pandem 0.636306 0.554316 0.423214 0.524195 0.425285 0.314649
health 0.612924 0.498453 0.38486 0.497694 0.389054 0.272496
peopl 0.655583 0.578072 0.449317 0.623015 0.432081 0.344397
impact 0.590031 0.461026 0.377568 0.482763 0.328156 0.222522
social 0.690754 0.672743 0.586466 0.703538 0.569479 0.510654
studi 0.579846 0.476091 0.34259 0.471397 0.386567 0.243858
research 0.595966 0.468226 0.372694 0.522492 0.323053 0.243918
imag 0.781713 0.844705 0.812935 0.905108 0.681579 0.732506
model 0.919469 0.828103 0.551424 0.815302 0.653473 0.728691
dataset 0 0.727202 0.692372 0.781132 0.696688 0.61537
class 0.727202 0 0.624976 0.691328 0.577896 0.521271
perform 0.692372 0.624976 0 0.577586 0.429273 0.372983
learn 0.781132 0.691328 0.577586 0 0.6373 0.5271
detect 0.696688 0.577896 0.429273 0.6373 0 0.381119
use 0.61537 0.521271 0.372983 0.5271 0.381119 0
base 0.649234 0.467935 0.389069 0.542538 0.273419 0.249719
base
covid 0.505415
psycholog 0.210683
group 0.218844
pandem 0.264391
health 0.282322
peopl 0.372698
impact 0.224732
social 0.493317
studi 0.255434
research 0.218313
imag 0.723226
model 0.663673
dataset 0.649234
class 0.467935
perform 0.389069
learn 0.542538
detect 0.273419
use 0.249719
base 0
Pairwise-distance
covid psycholog group pandem health peopl \
covid 0 0.665039 0.949824 0.512287 0.798946 0.722569
psycholog 0.665039 0 0.916289 0.500813 0.645733 0.689255
group 0.949824 0.916289 1.49012e-08 0.869534 1.01224 0.778218
pandem 0.512287 0.500813 0.869534 0 0.561819 0.587776
health 0.798946 0.645733 1.01224 0.561819 0 0.846159
peopl 0.722569 0.689255 0.778218 0.587776 0.846159 0
impact 0.556492 0.538323 0.846016 0.33904 0.661179 0.576973
social 0.645589 0.607703 0.734502 0.521936 0.690599 0.479194
studi 0.428616 0.526824 0.775837 0.477865 0.719016 0.652853
research 0.718988 0.462598 0.925893 0.676573 0.830778 0.763131
imag 0.569647 0.505794 0.778182 0.365353 0.627794 0.49626
model 0.545144 0.519223 0.800488 0.386536 0.641427 0.507928
dataset 0.574902 0.496188 0.774211 0.371155 0.62841 0.494985
class 0.577615 0.497861 0.771704 0.373614 0.631885 0.482538
perform 0.617221 0.527324 0.807069 0.408744 0.667675 0.544408
learn 0.565854 0.512273 0.781439 0.339772 0.639181 0.501184
detect 0.573687 0.49589 0.775558 0.367129 0.630211 0.497541
use 0.587577 0.463649 0.802866 0.368907 0.663655 0.503238
base 0.538749 0.517056 0.804437 0.351464 0.652988 0.521802
impact social studi research imag model \
covid 0.556492 0.645589 0.428616 0.718988 0.569647 0.545144
psycholog 0.538323 0.607703 0.526824 0.462598 0.505794 0.519223
group 0.846016 0.734502 0.775837 0.925893 0.778182 0.800488
pandem 0.33904 0.521936 0.477865 0.676573 0.365353 0.386536
health 0.661179 0.690599 0.719016 0.830778 0.627794 0.641427
peopl 0.576973 0.479194 0.652853 0.763131 0.49626 0.507928
impact 0 0.549137 0.420103 0.597067 0.333984 0.34889
social 0.549137 0 0.49973 0.652226 0.376604 0.3623
studi 0.420103 0.49973 0 0.490161 0.306665 0.325089
research 0.597067 0.652226 0.490161 0 0.536332 0.559126
imag 0.333984 0.376604 0.306665 0.536332 6.58545e-10 0.13642
model 0.34889 0.3623 0.325089 0.559126 0.13642 0
dataset 0.329749 0.387021 0.307446 0.529718 0.0360889 0.122277
class 0.332517 0.39049 0.307044 0.530599 0.0460422 0.134249
perform 0.391658 0.43571 0.38509 0.589779 0.205748 0.245633
learn 0.31445 0.406851 0.322133 0.532634 0.104919 0.162804
detect 0.330576 0.38691 0.303934 0.529531 0.0387109 0.126376
use 0.37528 0.357205 0.343383 0.522696 0.152451 0.125648
base 0.346954 0.376571 0.313572 0.509576 0.12651 0.206961
dataset class perform learn detect use \
covid 0.574902 0.577615 0.617221 0.565854 0.573687 0.587577
psycholog 0.496188 0.497861 0.527324 0.512273 0.49589 0.463649
group 0.774211 0.771704 0.807069 0.781439 0.775558 0.802866
pandem 0.371155 0.373614 0.408744 0.339772 0.367129 0.368907
health 0.62841 0.631885 0.667675 0.639181 0.630211 0.663655
peopl 0.494985 0.482538 0.544408 0.501184 0.497541 0.503238
impact 0.329749 0.332517 0.391658 0.31445 0.330576 0.37528
social 0.387021 0.39049 0.43571 0.406851 0.38691 0.357205
studi 0.307446 0.307044 0.38509 0.322133 0.303934 0.343383
research 0.529718 0.530599 0.589779 0.532634 0.529531 0.522696
imag 0.0360889 0.0460422 0.205748 0.104919 0.0387109 0.152451
model 0.122277 0.134249 0.245633 0.162804 0.126376 0.125648
dataset 2.85158e-10 0.0254673 0.204009 0.0969234 0.0161445 0.14166
class 0.0254673 0 0.206249 0.0967058 0.0262711 0.146868
perform 0.204009 0.206249 0 0.221897 0.205531 0.252859
learn 0.0969234 0.0967058 0.221897 0 0.0863365 0.172499
detect 0.0161445 0.0262711 0.205531 0.0863365 2.32831e-10 0.139091
use 0.14166 0.146868 0.252859 0.172499 0.139091 0
base 0.147138 0.152011 0.228732 0.122953 0.141612 0.211245
base
covid 0.538749
psycholog 0.517056
group 0.804437
pandem 0.351464
health 0.652988
peopl 0.521802
impact 0.346954
social 0.376571
studi 0.313572
research 0.509576
imag 0.12651
model 0.206961
dataset 0.147138
class 0.152011
perform 0.228732
learn 0.122953
detect 0.141612
use 0.211245
base 0
(-0.4187028966996108, 0.9848, -1.9122287293861753)
def load_data(path,file_name):
"""
Input : path and file_name
Purpose: loading text file
Output : list of paragraphs/documents and
title(initial 100 words considred as title of document)
"""
documents_list = []
titles=[]
document = ''
i=0
with open( os.path.join(path, file_name) ,"r",encoding="utf8") as fin:
data = fin.read()
words = data.split()
fin.seek(0)
print('Number of words in text file :', len(words))
for word in words:
i=i+1
document = document+word+' '
if i == 200:
documents_list.append(document)
document,i = '',0
if len(documents_list) == 2:
break
print("Total Number of Documents:",len(documents_list))
#titles.append( text[0:min(len(text),100)] )
return documents_list,titles,len(words)
document_list,titles,word_count_science=load_data("","covid-science-15.txt")
Number of words in text file : 88023 Total Number of Documents: 2
clean_text=preprocess_data(document_list)
['covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', 'tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herrera', 'abstract', 'currently', 'coronavirus', 'disease', 'covid', 'infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans', 'chest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'medical', 'centers', 'hence', 'cases', 'images', 'become', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networks', 'great', 'potential', 'building', 'covid', 'triage', 'systems', 'detecting', 'covid', 'patients', 'especially', 'patients', 'severity', 'unfortunately', 'current', 'databases', 'allow', 'building', 'systems', 'highly', 'heterogeneous', 'biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved', 'recent', 'covid', 'classification', 'models', 'under', 'close', 'collaboration', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levels', 'manuscript', 'received', 'september', 'revised', 'october', 'accepted', 'november'] ['publication', 'november', 'current', 'version', 'december', 'supported', 'project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid', 'spanish', 'ministry', 'science', 'technology', 'under', 'project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal', 'programme', '18136', 'gómez', 'supported', 'programme', 'fpu16', '04765', 'charte', 'supported', 'programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research', 'council', 'grant', 'agreement', '647038', 'biodesert', 'project', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'andalusian', 'research', 'institute', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', '36310', 'spain', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'atlanttic', 'research', 'center']
# LSA Model
number_of_topics=2
words_nscience=len(clean_text[0])+len(clean_text[1])
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words_nscience)
words_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values
#These variables are for further experimentation in vertical comparission mantel test
model_science_p=create_gensim_lsa_model(clean_text,number_of_topics,words_nscience)
words_nscience
238
dict(model_science.show_topic(0, topn=5))
{'support': -0.3289947178827894,
'granada': -0.24814037209037276,
'covid': -0.22769581399154298,
'programm': -0.2193298119218596,
'research': -0.2193298119218596}
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph23.png')
# LSA Model
a=pd.DataFrame()
for i in range(number_of_topics):
words_science =dict(model_science.show_topic(i, topn = words_nscience))
b=pd.DataFrame(words_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_science.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled26.png')
cosine-similarity
support granada covid programm research
support 1 0.956969 -0.0794645 1 1
granada 0.956969 1 0.213226 0.956969 0.956969
covid -0.0794645 0.213226 1 -0.0794645 -0.0794645
programm 1 0.956969 -0.0794645 1 1
research 1 0.956969 -0.0794645 1 1
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig26.png')
document_list,titles,word_count_psy=load_data("","covid-psychology-15.txt")
Number of words in text file : 58236 Total Number of Documents: 2
clean_text=preprocess_data(document_list)
['international', 'sociology', 'author', 'article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissions', '0268580920948807', 'journals', 'sagepub', 'psychology', 'politics', 'covid', 'misinfodemics', 'people', 'believe', 'misinfodemics', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'lahore', 'pakistan', 'abstract', 'misinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adverse', 'health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate', 'evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects', 'misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier', 'about', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article', 'review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article', 'shows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive', 'crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications', 'development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywords', 'conspiracy', 'theories', 'covid'] ['health', 'communication', 'misinformation', 'mistrust', 'science', 'mechanisms', 'misinfodemics', 'modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surrounding', 'corresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', '948807iss0010', '0268580920948807international', 'sociologymukhtar', 'research', 'article2020', 'article', 'international', 'sociology', 'covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern', 'globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during', 'lockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'become', 'major', 'source', 'interaction', 'holman', 'covid', 'pandemic', 'outbreak', 'escalated', 'challenges', 'healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerable', 'misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science', 'times', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'false', 'context', 'manipulated']
# LSA Model
number_of_topics=2
words_npsy=len(clean_text[0])+len(clean_text[1])
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words_npsy)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values
#These variables are for further experimentation in vertical comparission mantel test
model_psy_p = create_gensim_lsa_model(clean_text,number_of_topics,words_npsy)
dict(model_psy.show_topic(0, topn=5))
{'misinfodem': 0.5033743541941376,
'covid': 0.3629711503323517,
'articl': 0.21459251935197443,
'commun': 0.14837863098037718,
'mechan': 0.1483786309803767}
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph24.png')
# LSA Model
a=pd.DataFrame()
for i in range(number_of_topics):
words_psy =dict(model_psy.show_topic(i, topn=words_npsy))
b=pd.DataFrame(words_psy,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_psy.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled27.png')
cosine-similarity
misinfodem covid articl commun mechan
misinfodem 1 0.976353 0.993795 0.755596 0.755596
covid 0.976353 1 0.946251 0.879335 0.879335
articl 0.993795 0.946251 1 0.678051 0.678051
commun 0.755596 0.879335 0.678051 1 1
mechan 0.755596 0.879335 0.678051 1 1
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig27.png')
mantel_test(5)
Pairwise-distance
misinfodem covid articl commun mechan support granada \
misinfodem 0 0 0 0 0 0 0
covid 0 0 0.378443 0 0 0.628979 0.47364
articl 0 0.378443 0 0 0 0.423546 0.266413
commun 0 0 0 0 0 0 0
mechan 0 0 0 0 0 0 0
support 0 0.628979 0.423546 0 0 0 0.168269
granada 0 0.47364 0.266413 0 0 0.168269 0
programm 0 0.545218 0.293443 0 0 0.133207 0.0775087
research 0 0.545218 0.293443 0 0 0.133207 0.0775087
programm research
misinfodem 0 0
covid 0.545218 0.545218
articl 0.293443 0.293443
commun 0 0
mechan 0 0
support 0.133207 0.133207
granada 0.0775087 0.0775087
programm 0 0
research 0 0
Pairwise-distance
misinfodem covid articl commun mechan support granada \
misinfodem 0 0.191325 0.296807 0.422574 0.422574 0 0
covid 0.191325 0 0.160587 0.236438 0.236438 0 0
articl 0.296807 0.160587 0 0.173786 0.173786 0 0
commun 0.422574 0.236438 0.173786 0 0 0 0
mechan 0.422574 0.236438 0.173786 0 0 0 0
support 0 0 0 0 0 0 0
granada 0 0 0 0 0 0 0
programm 0 0 0 0 0 0 0
research 0.533903 0.353741 0.2551 0.121646 0.121646 0 0
programm research
misinfodem 0 0.533903
covid 0 0.353741
articl 0 0.2551
commun 0 0.121646
mechan 0 0.121646
support 0 0
granada 0 0
programm 0 0
research 0 0
(-0.12443276806076488, 0.6549, -0.511434675696413)
mantel_test(10)
Pairwise-distance
misinfodem covid articl commun mechan health strategi \
misinfodem 0 0 0 0 0 0 0
covid 0 0 0.378443 0 0 0 0
articl 0 0.378443 0 0 0 0 0
commun 0 0 0 0 0 0 0
mechan 0 0 0 0 0 0 0
health 0 0 0 0 0 0 0
strategi 0 0 0 0 0 0 0
psycholog 0 0 0 0 0 0 0
misinform 0 0 0 0 0 0 0
public 0 0.465052 0.112805 0 0 0 0
support 0 0.628979 0.423546 0 0 0 0
granada 0 0.47364 0.266413 0 0 0 0
programm 0 0.545218 0.293443 0 0 0 0
research 0 0.545218 0.293443 0 0 0 0
tabik 0 0.436747 0.19981 0 0 0 0
garcía 0 0.331184 0.138524 0 0 0 0
project 0 0.511267 0.22973 0 0 0 0
decsai 0 0.511267 0.22973 0 0 0 0
gómez 0 0.407471 0.133207 0 0 0 0
psycholog misinform public support granada programm \
misinfodem 0 0 0 0 0 0
covid 0 0 0.465052 0.628979 0.47364 0.545218
articl 0 0 0.112805 0.423546 0.266413 0.293443
commun 0 0 0 0 0 0
mechan 0 0 0 0 0 0
health 0 0 0 0 0 0
strategi 0 0 0 0 0 0
psycholog 0 0 0 0 0 0
misinform 0 0 0 0 0 0
public 0 0 0 0.333016 0.197705 0.19981
support 0 0 0.333016 0 0.168269 0.133207
granada 0 0 0.197705 0.168269 0 0.0775087
programm 0 0 0.19981 0.133207 0.0775087 0
research 0 0 0.19981 0.133207 0.0775087 0
tabik 0 0 0.138524 0.22973 0.0666033 0.112805
garcía 0 0 0.154608 0.336537 0.168269 0.22561
project 0 0 0.133207 0.19981 0.0903451 0.0666033
decsai 0 0 0.133207 0.19981 0.0903451 0.0666033
gómez 0 0 0.0903451 0.293443 0.133207 0.168269
research tabik garcía project decsai gómez
misinfodem 0 0 0 0 0 0
covid 0.545218 0.436747 0.331184 0.511267 0.511267 0.407471
articl 0.293443 0.19981 0.138524 0.22973 0.22973 0.133207
commun 0 0 0 0 0 0
mechan 0 0 0 0 0 0
health 0 0 0 0 0 0
strategi 0 0 0 0 0 0
psycholog 0 0 0 0 0 0
misinform 0 0 0 0 0 0
public 0.19981 0.138524 0.154608 0.133207 0.133207 0.0903451
support 0.133207 0.22973 0.336537 0.19981 0.19981 0.293443
granada 0.0775087 0.0666033 0.168269 0.0903451 0.0903451 0.133207
programm 0 0.112805 0.22561 0.0666033 0.0666033 0.168269
research 0 0.112805 0.22561 0.0666033 0.0666033 0.168269
tabik 0.112805 0 0.112805 0.0775087 0.0775087 0.0666033
garcía 0.22561 0.112805 0 0.181739 0.181739 0.0775087
project 0.0666033 0.0775087 0.181739 0 0 0.112805
decsai 0.0666033 0.0775087 0.181739 0 0 0.112805
gómez 0.168269 0.0666033 0.0775087 0.112805 0.112805 0
Pairwise-distance
misinfodem covid articl commun mechan health \
misinfodem 0 0.191325 0.296807 0.422574 0.422574 0.422574
covid 0.191325 0 0.160587 0.236438 0.236438 0.236438
articl 0.296807 0.160587 0 0.173786 0.173786 0.173786
commun 0.422574 0.236438 0.173786 0 0 0
mechan 0.422574 0.236438 0.173786 0 0 0
health 0.422574 0.236438 0.173786 0 0 0
strategi 0.364939 0.24088 0.0802933 0.191551 0.191551 0.191551
psycholog 0.364939 0.24088 0.0802933 0.191551 0.191551 0.191551
misinform 0.510199 0.318875 0.290918 0.118949 0.118949 0.118949
public 0.41268 0.243293 0.12755 0.0789346 0.0789346 0.0789346
support 0 0 0 0 0 0
granada 0 0 0 0 0 0
programm 0 0 0 0 0 0
research 0.533903 0.353741 0.2551 0.121646 0.121646 0.121646
tabik 0 0 0 0 0 0
garcía 0 0 0 0 0 0
project 0 0 0 0 0 0
decsai 0 0 0 0 0 0
gómez 0 0 0 0 0 0
strategi psycholog misinform public support granada \
misinfodem 0.364939 0.364939 0.510199 0.41268 0 0
covid 0.24088 0.24088 0.318875 0.243293 0 0
articl 0.0802933 0.0802933 0.290918 0.12755 0 0
commun 0.191551 0.191551 0.118949 0.0789346 0 0
mechan 0.191551 0.191551 0.118949 0.0789346 0 0
health 0.191551 0.191551 0.118949 0.0789346 0 0
strategi 0 0 0.30895 0.118949 0 0
psycholog 0 0 0.30895 0.118949 0 0
misinform 0.30895 0.30895 0 0.191551 0 0
public 0.118949 0.118949 0.191551 0 0 0
support 0 0 0 0 0 0
granada 0 0 0 0 0 0
programm 0 0 0 0 0 0
research 0.233213 0.233213 0.145904 0.12755 0 0
tabik 0 0 0 0 0 0
garcía 0 0 0 0 0 0
project 0 0 0 0 0 0
decsai 0 0 0 0 0 0
gómez 0 0 0 0 0 0
programm research tabik garcía project decsai gómez
misinfodem 0 0.533903 0 0 0 0 0
covid 0 0.353741 0 0 0 0 0
articl 0 0.2551 0 0 0 0 0
commun 0 0.121646 0 0 0 0 0
mechan 0 0.121646 0 0 0 0 0
health 0 0.121646 0 0 0 0 0
strategi 0 0.233213 0 0 0 0 0
psycholog 0 0.233213 0 0 0 0 0
misinform 0 0.145904 0 0 0 0 0
public 0 0.12755 0 0 0 0 0
support 0 0 0 0 0 0 0
granada 0 0 0 0 0 0 0
programm 0 0 0 0 0 0 0
research 0 0 0 0 0 0 0
tabik 0 0 0 0 0 0 0
garcía 0 0 0 0 0 0 0
project 0 0 0 0 0 0 0
decsai 0 0 0 0 0 0 0
gómez 0 0 0 0 0 0 0
(-0.15851516925283998, 0.8535, -1.0488047694214804)
def load_data(path,file_name):
"""
Input : path and file_name
Purpose: loading text file
Output : list of paragraphs/documents and
title(initial 100 words considred as title of document)
"""
documents_list = []
titles=[]
with open( os.path.join(path, file_name) ,"r",encoding='UTF8') as fin:
data = fin.read()
words = data.split()
fin.seek(0)
print('Number of words in text file :', len(words))
for line in fin.readlines():
text = line.strip()
if text !='':
documents_list.append(text)
print("Total Number of Documents:",len(documents_list))
titles.append( text[0:min(len(text),100)] )
return documents_list,titles,len(words)
document_list,titles,word_count_science=load_data("","covid-science-15.txt")
Number of words in text file : 88023 Total Number of Documents: 10436
clean_text=preprocess_data(document_list)
['covidgr', 'dataset', 'covid', 'sdnet'] ['methodology', 'predicting', 'covid'] ['based', 'chest', 'images'] ['tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte'] ['guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova'] ['olmedo', 'sánchez', 'herrera'] ['abstract', 'currently', 'coronavirus', 'disease', 'covid'] ['infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans'] ['chest', 'images', 'computed', 'tomography'] ['scanners', 'testing', 'available'] ['medical', 'centers', 'hence', 'cases', 'images'] ['become', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networks'] ['great', 'potential', 'building', 'covid', 'triage', 'systems'] ['detecting', 'covid', 'patients', 'especially', 'patients'] ['severity', 'unfortunately', 'current', 'databases', 'allow'] ['building', 'systems', 'highly', 'heterogeneous'] ['biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved'] ['recent', 'covid', 'classification', 'models', 'under'] ['close', 'collaboration', 'hospital', 'universitario', 'clínico'] ['cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levels'] ['manuscript', 'received', 'september', 'revised', 'october'] ['accepted', 'november', 'publication', 'november'] ['current', 'version', 'december', 'supported'] ['project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundación'] ['equipos', 'investigación', 'científica', 'covid'] ['spanish', 'ministry', 'science', 'technology', 'under'] ['project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal'] ['programme', '18136', 'gómez', 'supported'] ['programme', 'fpu16', '04765', 'charte', 'supported'] ['programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research'] ['council', 'grant', 'agreement', '647038', 'biodesert', 'project'] ['approved', 'provincial', 'research', 'ethics', 'committee', 'granada'] ['corresponding', 'author', 'siham', 'tabik'] ['tabik', 'gómez', 'sevillano', 'garcía', 'charte', 'suárez'] ['luengo', 'herrera', 'andalusian', 'research', 'institute'] ['science', 'computational', 'intelligence', 'university', 'granada'] ['18071', 'granada', 'spain', 'siham', 'anabelgrios', 'decsai'] ['isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz'] ['julianlm', 'decsai', 'herrera', 'decsai'] ['martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova'] ['olmedo', 'sánchez', 'hospital', 'universitario', 'clínico'] ['cecilio', 'granada', '36310', 'spain', 'joseluismartin'] ['hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail'] ['euolm', 'yahoo'] ['atlanttic', 'research', 'center', 'telecommunication', 'technologies', 'university', 'galicia', 'spain'] ['mreyarea', 'gmail'] ['guirado', 'multidisciplinary', 'institute', 'environment'] ['studies', 'ramón', 'margalef', 'university', 'alicante', '03690', 'spain'] ['geesecillo', 'gmail'] ['digital', 'object', 'identifier', '3037127'] ['severity', 'normal', 'positive', 'moderate', 'severe', 'covidgr', 'contains', 'positive'] ['negative', 'posteroanterior', 'views'] ['propose', 'covid', 'smart', 'based', 'network', 'covid', 'sdnet'] ['methodology', 'improving', 'generalization', 'capacity'] ['covid', 'classification', 'models', 'approach', 'reaches'] ['stable', 'results', 'accuracy'] ['severe', 'moderate'] ['covid', 'severity', 'levels', 'approach', 'could'] ['early', 'detection', 'covid', 'covidgr', 'along'] ['severity', 'level', 'labels', 'available', 'scientific', 'community', 'through', 'https', 'dasci', 'transferencia'] ['covidgr'] ['index', 'terms', 'covid', 'convolutional', 'neural', 'networks'] ['smart'] ['introduction'] [] ['months', 'world', 'witnessing'] ['covid', 'pandemic', 'increasingly', 'infecting', 'large'] ['people', 'everywhere', 'world', 'trends'] ['clear', 'research', 'confirm', 'problem'] ['persist', 'until', 'besides', 'prevalence', 'studies', 'conducted'] ['several', 'countries', 'reveal', 'proportion', 'population'] ['developed', 'antibodies', 'after', 'exposure', 'virus'] ['spain', 'means', 'frequently', 'large', 'number', 'patients'] ['assessed', 'small', 'intervals', 'number'] ['clinicians', 'resources'] ['general', 'covid', 'diagnosis', 'carried', 'using', 'least'] ['these', 'three', 'tests', 'computed', 'tomography', 'scans', 'based', 'assessment'] ['consists', 'analyzing', 'radiographic', 'images', 'different', 'angles', 'needed', 'equipment', 'assessment'] ['available', 'hospitals', 'takes'] ['minutes', 'patient', 'addition', 'required'] ['decontamination'] ['reverse', 'transcription', 'polymerase', 'chain', 'reaction', 'rtpcr', 'detects', 'viral', 'sputum'] ['online', 'available', 'https', 'english', 'elpais', 'society'] ['antibody', 'study', 'shows', 'spaniards', 'contracted', 'coronavirus'] ['online', 'available', 'advocacy', 'economics', 'acrposition', 'statements', 'recommendations', 'chest', 'radiography', 'forsuspected', 'covid19', 'infection'] ['article', 'access', 'download', 'along', 'rights', 'mining', 'analysis'] ['authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'xplore', 'restrictions', 'apply'] ['journal', 'biomedical', 'health', 'informatics', 'december'] ['stratification', 'radiological', 'severity', 'covid', 'examples', 'index', 'calculated'] ['nasopharyngeal', 'requires', 'specific', 'material'] ['equipment', 'which', 'easily', 'accessible', 'takes'] ['least', 'hours', 'which', 'desirable', 'positive', 'covid19', 'patients', 'should', 'identified', 'tracked', 'possible', 'studies', 'found', 'results', 'several'] ['tests', 'different', 'points', 'patients', 'variable', 'during', 'course', 'illness', 'producing', 'falsenegative', 'authors', 'suggested'] ['should', 'combined', 'other', 'clinical', 'tests'] ['chest', 'required', 'equipment'] ['assessment', 'cumbersome', 'lightweight'] ['transportable', 'general', 'resources'] ['available', 'required', 'tests'] ['addition', 'takes', 'about', 'seconds', 'patient'] ['which', 'makes', 'effective'] ['assessment', 'tools'] ['recent', 'studies', 'provide', 'estimates', 'expert', 'radiologists'] ['sensitivity', 'diagnosis', 'covid', 'based', 'scans'] ['study', 'patients', 'chest'] ['essay', 'performed', 'within', 'reported'] ['sensitivity', 'compared', 'sensitivity'] ['different', 'study', 'patients'] ['years', 'reported', 'sensitivity', 'compared'] ['initial', 'according', 'analysis'] ['ambulatory', 'patients', 'patients', 'presenting', 'urgent'] ['centers', 'confirmed', 'coronavirus', 'disease', 'normal'] ['mildly', 'abnormal', 'findings', 'these', 'patients'] ['correctly', 'diagnosed', 'expert'] ['recent', 'study', 'authors', 'proposed', 'simplifying', 'quantification', 'level', 'severity', 'adapting', 'previously', 'defined'] ['radiographic', 'assessment', 'edema', 'score'] ['covid', 'score', 'calculated', 'assigning', 'value'] ['between', 'depending', 'extent', 'visual'] ['features', 'consolidation', 'ground', 'glass', 'opacities'] ['parts', 'depicted', 'based', 'score'] ['experts', 'identify', 'level', 'severity', 'infection', 'among'] ['severity', 'stages', 'normal', 'moderate'] ['severe', 'practice', 'patient', 'classified', 'expert', 'radiologist'] ['authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'xplore', 'restrictions', 'apply'] ['tabik', 'covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images'] ['normal', 'positive', 'refer', 'these', 'cases'] ['normal', 'expert', 'annotation', 'adopted', 'based'] ['score'] ['automated', 'image', 'analysis', 'learning', 'models'] ['great', 'potential', 'optimize', 'images'] ['diagnosis', 'covid', 'robust', 'accurate'] ['model', 'could', 'serve', 'triage', 'method', 'support'] ['medical', 'decision', 'making', 'increasing', 'number', 'recent', 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'represents', 'shows', 'fewer'] ['differences', 'between', 'cases', 'control', 'patient', 'histograms'] ['column', 'corresponding', 'chexpert', 'dataset', 'these'] ['differences', 'evident'] ['could', 'imply', 'datasets', 'bimcv'] ['machine', 'learning', 'algorithm', 'classify', 'pneumo', 'control', 'cases', 'using', 'features', 'outside', 'lungs'] ['network'] ['article', 'convolutional', 'neural', 'networks'] ['classify', 'images', 'these', 'machine', 'learn', 'models', 'widely', 'employed', 'years'] ['image', 'classification', 'particularly', 'field', 'medical'] ['imaging', 'topology', 'vgg16', 'which'] ['broadly', 'reported', 'classifier', 'chest', 'image'] ['analysis', 'scenario', 'common', 'practice'] ['networks', 'layers', 'usually'] ['dense', 'layers', 'lighter', 'classifier', 'which'] ['volume', '42373o', 'catalá', 'analysis', 'public', 'image', 'datasets'] ['figure', 'example', 'control', 'patient', 'histograms', 'first', 'shows', 'histogram', 'whole', 'image', 'example'] ['control', 'patient', 'second', 'shows', 'histogram', 'background', 'image', 'subtracted', 'shows'] ['histogram', 'lungs'] ['global', 'average', 'pooling', 'followed', 'multilayer'] ['perceptron', 'which', 'projects', 'pooled', 'features'] ['convolution', 'dimensions', 'before', 'performing'] ['classification'] ['transfer', 'learning', 'technique', 'common', 'practice', 'within'] ['learning', 'models', 'proven', 'pretrained', 'works', 'particular', 'their', 'first', 'layers', 'generic'] ['transferred', 'domains', 'without', 'requiring'] ['special', 'training', 'facilitates', 'training'] ['domains', 'scarce', 'amount', 'training', 'samples', 'there', 'vgg16', 'network', 'pretrained', 'genet', 'dataset', 'convolutional', 'layers', 'along'] ['classification', 'layers', 'unfrozen', 'domain'] ['training'] ['noteworthy', 'network', 'structure'] ['point', 'critical', 'conclusions', 'drawn', 'article'] ['trying', 'present', 'advancement', 'state', 'classification', 'datasets', 'focus', 'rather'] ['comparing', 'results', 'obtained', 'images', 'coming'] ['different', 'datasets', 'whether', 'those', 'results', 'suggest', 'classification', 'biases', 'within', 'nonetheless'] ['least', 'achieve', 'acceptable', 'accuracy', 'order'] ['ensure', 'extracted', 'features', 'enough', 'close'] ['extracted', 'other', 'articles'] ['segmentation'] ['segmenting', 'lungs', 'possible', 'remove', 'parts'] ['image', 'contain', 'relevant', 'information'] ['source', 'noise', 'presence'] ['annotations', 'identify', 'machine', 'hospital'] ['appearance', 'images', 'coming', 'specific', 'medical', 'devices'] ['cases', 'control', 'patients'] ['versa'] ['segmentation', 'images', 'successfully'] ['tackled', 'different', 'approaches', 'during', 'years'] ['network', 'trained'] ['montgomery', 'dataset', 'moreover', 'manually'] ['labeled', 'total', 'images', 'coming', 'bimcv'] ['42374', 'volume', '2021o', 'catalá', 'analysis', 'public', 'image', 'datasets'] ['figure', 'image', 'histogram', 'variability', 'first', 'represents', 'variability', 'histograms', 'complete', 'images', 'second', 'variability'] ['background', 'histograms', 'images', 'subtracted', 'third', 'histograms', 'lungs', 'first', 'column', 'represents'] ['sample', 'bimcv', 'dataset', 'second', 'column', 'sample', 'sample', 'chexpert'] ['padchest', 'dataset', 'increase', 'number', 'training', 'images'] ['figure', 'shows', 'segmentation', 'results', 'network'] ['achieves', 'scores', 'gomery', 'partition', 'where', 'defined'] ['follows', 'being', 'predicted', 'segmentation'] ['segmentation'] [] [] [] [] [] [] ['volume', '42375o', 'catalá', 'analysis', 'public', 'image', 'datasets'] ['analysis'] ['proposes', 'methodology', 'measure', 'degree'] ['dataset', 'focus', 'classification', 'monia', 'covid', 'against', 'control', 'samples', 'methods'] ['generalized', 'other', 'classification', 'tasks', 'where', 'prior'] ['knowledge', 'region', 'interest', 'available'] ['stated', 'before', 'areas', 'should', 'contain', 'informa', 'about', 'problem', 'possibly', 'discriminate'] ['between', 'classes', 'example', 'annotations', 'image', 'tures', 'related', 'medical', 'devices', 'employed', 'order'] ['solve', 'problem', 'segmentation', 'algorithm'] ['extract', 'relevant', 'regions', 'which', 'lungs'] ['figure', 'these', 'regions', 'referred', 'masks'] ['image', 'considered', 'background'] ['figure'] ['figure', 'segmentation', 'after', 'process', 'right'] ['check', 'previous', 'hypothesis', 'presented'] ['experiments', 'carried', 'training', 'model'] ['different', 'image', 'areas', 'according', 'following'] ['ideas'] ['study', 'background', 'affects', 'results'] ['starting', 'image', 'contains', 'lungs'] ['background', 'erased', 'visible', 'region', 'gressively', 'expanded', 'include', 'background'] ['means', 'sequential', 'dilation', 'operations'] ['figure', 'unbiased', 'dataset', 'should', 'increase'] ['classification', 'accuracy', 'along', 'process'] ['analyze', 'affects'] ['results', 'starting', 'whole', 'image'] ['progressively', 'removing', 'lungs', 'figure'] ['classification', 'accuracy', 'unbiased', 'dataset', 'should'] ['progressively', 'maximum', 'value', 'whole'] ['image'] ['adjusting', 'expansion', 'exclusion', 'region'] ['allow', 'trace', 'variation', 'accuracy', 'metric'] ['images', 'scaled', 'pixels', 'background'] ['expansion', 'segmentation', 'masks', 'dilated'] ['pixels', 'exclusion', 'masks'] ['eroded', 'pixels', 'right'] ['figure'] ['figure', 'shows', 'segmented'] ['background', 'expansion', 'green', 'figure', 'shows'] ['exclusion', 'yellow', 'additionally', 'detailed'] ['workflow', 'experiment', 'shown', 'figure'] ['combination', 'analysis'] ['combining', 'datasets', 'useful', 'enlarge', 'sample'] ['increase', 'variability', 'explained', 'reduce'] ['epistemic', 'uncertainty', 'classifiers', 'latter', 'related'] ['problem', 'domain', 'knowledge', 'model', 'being'] ['uncertainty', 'knowledge', 'bound', 'limited', 'amount'] ['however', 'combination', 'balance', 'among'] ['classes', 'carefully', 'controlled', 'classifier', 'learn'] ['discriminate', 'between', 'features', 'different', 'datasets'] ['check', 'hypothesis', 'mixed', 'chexpert'] ['datasets', 'achieve', 'balanced', 'combination', 'adding', 'positive'] ['pneumonia', 'observations', 'dataset', 'latter', 'highly', 'unbalanced', 'dataset', 'ative', 'positive', 'observations', 'after', 'process'] ['segmentation', 'validity', 'filters', 'could', 'considered'] ['positive', 'samples', 'another', 'dataset'] ['needless', 'images', 'distinct'] ['features', 'allow', 'classifier', 'apart'] ['chexpert', 'example', 'including', 'large', 'proportion', 'images'] ['particular', 'equipment', 'brand', 'model', 'system'] ['learn', 'classify', 'images', 'equipment', 'positive'] ['regardless', 'image', 'content', 'could', 'related'] ['disease'] ['additionally', 'simulated', 'combination'] ['covid', 'control', 'datasets', 'evaluated', 'their'] ['proposed', 'method', 'particular', 'datasets', 'bined', 'positive', 'covid', 'cases', 'covidcxr'] ['chexpert', 'negative', 'control', 'samples', 'covidcxr', 'built'] ['datasets', 'different', 'origins', 'hence', 'experiment', 'illus', 'trates', 'likely', 'problematic', 'effects', 'heterogeneous'] ['combinations'] ['based', 'methodology', 'probes', 'discrimination'] ['induced', 'outside', 'lungs', 'expectations', 'about', 'results'] ['experiment', 'there', 'dataset'] ['background', 'expansion', 'could', 'increase', 'accuracy'] ['accuracy', 'occluding', 'lungs', 'should', 'differ'] ['significantly', 'results', 'follow'] ['these', 'predictions', 'hypothesis', 'would', 'confirmed'] ['results'] ['background', 'expansion', 'exclusion'] ['study'] ['previous', 'section', 'proposed', 'examine', 'perfor', 'mance', 'classification', 'experiments', 'varying', 'addition'] ['background', 'reduction', 'expected'] ['results', 'first', 'biased', 'dataset', 'where'] ['background', 'added', 'initial', 'images'] ['classification', 'stays', 'constant', 'almost', 'stant', 'possible', 'imprecise', 'segmentation', 'other', 'perturbations', 'disease', 'information', 'already'] ['present', 'beginning'] ['second', 'scenario', 'accuracy', 'should', 'potentially'] ['value', 'achieved', 'network'] ['complete', 'image', 'value', 'close'] ['lungs', 'completely', 'removed', 'necessarily'] ['42376', 'volume', '2021o', 'catalá', 'analysis', 'public', 'image', 'datasets'] ['figure', 'background', 'expansion', 'exclusion', 'original', 'contour', 'shown', 'expanded', 'contour', 'green'] ['contour', 'removed', 'shown', 'yellow'] ['figure', 'analysis', 'workflow'] ['linear', 'shown', 'graphs', 'straight'] ['right', 'figure', 'offer', 'simplified'] ['graphical', 'representation', 'expected', 'behavior'] ['figure', 'green', 'represents', 'classification'] ['obtained', 'using'] ['analysis', 'performed', 'three', 'datasets'] ['first', 'figure', 'bimcv', 'clearly', 'shows'] ['significant', 'within', 'classification'] ['steadily', 'increases', 'background', 'expansion'] ['second', 'graph', 'shows', 'removing'] ['associated', 'significant', 'decrease', 'accuracy'] ['should', 'complete', 'exclusion'] ['lungs', 'classifier', 'achieves', 'almost'] ['second', 'figure', 'displays'] ['slightly', 'lower', 'still', 'consistent', 'within'] ['graphs', 'however', 'dataset', 'harder'] ['segment', 'other'] ['variability', 'shown', 'could', 'arise', 'poorly', 'segmented'] ['images', 'nonetheless', 'achieved'] ['lungs', 'completely', 'occluded', 'which'] ['expected'] ['third', 'figure', 'chexpert', 'conveys', 'inter', 'esting', 'results', 'graph', 'trend', 'expected'] ['unbiased', 'dataset', 'doesn', 'along'] ['background', 'expansion', 'nevertheless', 'precision'] ['achieved', 'completely', 'occluded'] ['around', 'implies'] ['located', 'specifically', 'background'] ['whole', 'image'] ['combination', 'study'] ['mentioned', 'before', 'combination', 'study', 'seeks', 'evaluate'] ['combination', 'datasets', 'might', 'provoke', 'creation'] ['biased', 'methodology', 'proposed', 'detect'] ['these', 'weaknesses', 'final', 'collection'] ['experiments', 'section', 'reproduced'] ['using', 'combined', 'dataset', 'figure', 'shows', 'effect'] ['varying', 'background', 'expansion', 'exclusion'] ['combination', 'designed', 'balance', 'chexpert'] ['cases', 'control', 'positive', 'pneumonia', 'images'] ['chexpert', 'positive', 'images', 'giving'] ['balanced', 'dataset', 'observations', 'class'] ['experiment', 'explored', 'combination', 'images'] ['control', 'patients', 'chexpert', 'whole'] ['covid', 'images', 'covidcxr', 'dataset'] ['combination', 'typical', 'recent', 'crisis', 'scenario', 'where'] ['images', 'disease', 'available', 'obtained'] ['different', 'locations', 'under', 'uncontrolled', 'conditions'] ['different', 'equipment', 'acquisition', 'protocols'] ['worst', 'scenario', 'results', 'accordance'] ['figure'] ['results', 'these', 'experiments', 'similar', 'fashion'] ['chexpert', 'ubiquitous'] ['volume', '42377o', 'catalá', 'analysis', 'public', 'image', 'datasets'] ['figure', 'accuracy', 'function', 'background', 'expansion', 'reduction', 'green', 'dotted', 'lines'] ['correct', 'behavior', 'biased', 'dataset', 'background', 'included'] ['dotted', 'lines', 'indicate', 'expected', 'reduction', 'classification', 'lungs', 'removed'] ['analysis', 'lines', 'accuracy', 'given', 'expansion', 'reduction', 'vertical', 'indicating'] ['standard', 'deviation'] ['image', 'despite', 'increasing', 'amount', 'background', 'inside'] ['images', 'doesn', 'affect', 'accuracy', 'effect'] ['occlusion', 'remarkable', 'within', 'results'] ['discussion'] ['learning', 'receiving', 'attention'] ['powerful', 'methodology', 'analyzing', 'medical'] ['images', 'ability', 'convolutional', 'neural', 'works', 'obtain', 'excellent', 'results'] ['blackbox', 'opposed', 'classical', 'design'] ['algorithms', 'attracted', 'researchers'] ['works', 'using', 'covid', 'detection'] ['images', 'report', 'accuracies', 'variety', 'network'] ['architectures', 'particular', 'studies', 'using', 'vgg16', 'report'] ['accuracy', 'dataset', 'built', 'covid'] ['control', 'samples', 'accuracy', 'obtained'] ['dataset', 'composed', 'covid', 'images', 'pneumo', 'negative', 'accuracy', 'achieved'] ['using', 'dataset', 'contains', 'covid', 'images'] ['pneumonia', 'negative', 'vgg16', 'achieves'] ['results', 'detecting', 'pulmonary', 'diseases', 'strengthens'] ['hypothesis', 'features', 'extracted', 'network'] ['relevant', 'therefore', 'detected'] ['experiments', 'related', 'within', 'images'] ['drawbacks', 'often'] ['large', 'amounts', 'learn', 'while', 'generic'] ['databases', 'available', 'public', 'existing', 'covid', 'datasets'] ['composed', 'images', 'collected'] ['volunteers', 'consequence', 'these', 'datasets'] ['unbalanced', 'labels', 'different', 'sources'] ['42378', 'volume', '2021o', 'catalá', 'analysis', 'public', 'image', 'datasets'] ['figure', 'addition', 'positive', 'samples', 'covidcxr', 'chexpert', 'dataset', 'green', 'dotted'] ['lines', 'correct', 'behavior', 'biased', 'dataset', 'background', 'included'] ['dotted', 'lines', 'indicate', 'expected', 'reduction', 'classification', 'lungs', 'removed'] ['analysis', 'lines', 'accuracy', 'given', 'expansion', 'reduction', 'vertical'] ['indicating', 'standard', 'deviation'] ['makes', 'getting', 'robust', 'model', 'reliable', 'performance'] ['measures', 'difficult', 'regard', 'articles', 'report'] ['problem', 'small', 'unbalanced', 'datasets', 'covid'] ['detection', 'propose', 'solutions', 'mitigate'] ['problem'] ['analysis', 'tackled', 'other', 'authors'] ['instance', 'authors', 'proposed', 'train'] ['partitions', 'should', 'different', 'datasets', 'related'] ['classifier', 'trying', 'achieve', 'maximum'] ['performance', 'certain', 'dataset'] ['assert', 'generalization', 'capacity', 'classi', 'other', 'sought', 'minimize', 'effects'] ['different', 'biased', 'datasets', 'converting', 'different'] ['dataset', 'observations', 'prototypes', 'greatly', 'reducing', 'possible'] ['intra', 'dataset', 'specific', 'features'] ['recently', 'addresses', 'issue', 'covid', 'detection'] ['reports', 'problem', 'mixing', 'different', 'datasets'] ['network', 'learn', 'background', 'information', 'study'] ['performs', 'similar', 'approach', 'presented', 'thisar', 'ticle', 'study', 'possible', 'biases', 'within', 'lungs'] ['occludes', 'lungs', 'rectangular', 'fixed', 'black', 'boxes'] ['measures', 'accuracy', 'achieved', 'however', 'proposed'] ['methodology', 'extends', 'concept', 'proposed', 'precise'] ['masks', 'progressive', 'inclusion', 'exclusion', 'information'] ['learning', 'process', 'allows', 'ability', 'detect'] ['where', 'approximately', 'enables', 'precise'] ['estimation'] ['furthermore', 'studies', 'within', 'ncov2019', 'dataset'] ['using', 'information', 'about', 'patients', 'symptoms', 'comorbidities'] ['dataset', 'collects', 'clinical', 'differ', 'sources', 'rather', 'images', 'found', 'significant'] ['related', 'origin', 'exposed', 'several', 'issues'] ['related', 'multisource', 'variability'] ['article', 'focused', 'detecting', 'biases', 'within'] ['widely', 'datasets', 'glimpse', 'degree', 'which'] ['these', 'biases', 'affect', 'results', 'proposes', 'detection'] ['methodology', 'assert', 'validity', 'results', 'methodol', 'makes', 'techniques', 'heatmap', 'visualization'] ['histogram', 'analysis', 'selective', 'image', 'occlusion', 'which'] ['combined', 'evaluate', 'which', 'parts', 'images', 'being'] ['discriminative', 'features', 'classification'] ['methodology', 'applied', 'scenar', 'existence', 'individual', 'pneumonia'] ['datasets', 'another', 'detect', 'existence'] ['datasets'] ['limitations', 'study'] ['regarding', 'possible', 'limitations', 'there', 'could', 'problem'] ['methodology', 'proposed', 'since', 'segmentation', 'masks'] ['expansion', 'reduction', 'biased', 'themselves'] ['segmentation', 'process', 'might', 'prone'] ['images', 'pneumonia', 'since', 'borders', 'lungs'] ['diffuse', 'whereas', 'could', 'happen', 'images'] ['control', 'patients', 'could', 'significant', 'difference'] ['volume', '42379o', 'catalá', 'analysis', 'public', 'image', 'datasets'] ['figure', 'occlusion', 'fixed', 'rectangular', 'boxes'] ['between', 'cases', 'controls', 'masks', 'therefore', 'might'] ['introducing', 'would', 'imply', 'problem'] ['proposed', 'methodology'] ['however', 'designed', 'experiment', 'where'] ['occlusion', 'masks', 'substituted', 'rectangles'] ['lungs', 'experiment', 'similar', 'presented'] ['ensure', 'lungs', 'completely'] ['removed', 'using', 'segmentation', 'shape', 'whereas'] ['aforementioned', 'place', 'fixed', 'black', 'angle', 'central', 'leaving', 'uncovered'] ['examples', 'method', 'figure'] ['results', 'achieved', 'bimcv', 'dataset'] ['figure', 'where', 'differences', 'found', 'significant'] ['suggesting', 'shape', 'masks', 'influencing'] ['detection', 'algorithm', 'proposed'] ['furthermore', 'increase', 'confidence', 'conclusions'] ['processed', 'images', 'means', 'clahe', 'togram', 'normalization', 'assert', 'process', 'affected'] ['results', 'figure', 'there', 'difference'] ['results', 'achieved', 'between', 'normalized', 'plain'] ['images'] ['talking', 'about', 'strengths', 'results', 'experiments'] ['described', 'section', 'demonstrated', 'classification'] ['improve', 'background', 'included'] ['images', 'which', 'means', 'either', 'there'] ['specifically', 'background', 'significant'] ['already', 'within', 'lungs', 'however'] ['progressively', 'removed', 'image'] ['experiments', 'accuracy', 'decrease', 'suggesting'] ['figure', 'comparison', 'between', 'grain', 'squared', 'masks', 'bimcv', 'dataset'] ['42380', 'volume', '2021o', 'catalá', 'analysis', 'public', 'image', 'datasets'] ['figure', 'comparison', 'between', 'normalized', 'plain', 'bimcv', 'dataset'] ['system', 'classifying', 'images', 'according'] ['elements', 'present', 'whole', 'image', 'inside'] ['lungs', 'result', 'confirms', 'hypothesis', 'powerful', 'convolutional', 'networks', 'subtle', 'features'] ['images', 'optimistic', 'classification', 'results'] ['measures', 'taken', 'avoid', 'biases'] ['summarize', 'further', 'research', 'should', 'conducted'] ['reduce', 'impact', 'intrinsic', 'datasets', 'whose'] ['images', 'collected', 'several', 'sources', 'recent', 'literature'] ['demonstrated', 'emergence', 'methodologies', 'useful'] ['reduce', 'impact', 'image', 'preprocess', 'methods', 'learning', 'architectures', 'designed'] ['biased', 'datasets', 'starting'] ['point'] ['conclusion'] ['novel', 'methodology', 'assess', 'existence'] ['image', 'datasets', 'presented', 'techniques'] ['activation', 'heatmap', 'visualization', 'histogram', 'analysis'] ['selective', 'image', 'occlusion', 'combined', 'evaluate', 'which'] ['images', 'being', 'discriminative', 'features'] ['classification', 'regions', 'interest'] ['lungs', 'datasets', 'different', 'levels'] ['these', 'comprising', 'datasets', 'informa', 'quickly', 'available', 'urgent', 'scenario', 'current'] ['covid', 'crisis', 'examples', 'bimcv', 'collection'] ['combination', 'datasets', 'created', 'purpose', 'which'] ['problems', 'results', 'confirmed'] ['other', 'methodologies'] ['activation', 'histogram', 'analysis'] ['study', 'effects', 'combining', 'datasets', 'differ', 'sources', 'especially', 'interesting', 'because', 'shows'] ['strictly', 'controlled', 'important', 'biases', 'induced'] ['final', 'dataset', 'typical', 'solution', 'samples'] ['given', 'class', 'compile', 'different', 'datasets'] ['collects', 'categories', 'study', 'recent', 'covid'] ['datasets', 'particular', 'widely', 'covidcxr', 'dataset'] ['built', 'different', 'sources', 'might', 'included'] ['significant', 'biases', 'inadvertently', 'affected', 'results', 'lished', 'heterogeneous', 'dataset', 'often', 'observa', 'tions', 'coming', 'diverse', 'equipment', 'acquisition', 'proto', 'processing', 'software', 'context', 'features', 'found'] ['convolutional', 'networks', 'images', 'including'] ['background', 'areas', 'enough', 'classification'] ['whilst', 'actual', 'performance', 'classifier', 'clinical'] ['attempted', 'lower'] ['acknowledgments'] ['authors', 'would', 'thanks', 'gratitude', 'bimcv'] ['other', 'teams', 'compiled', 'available'] ['datasets', 'experiments', 'ducted', 'employing', 'instituto', 'tecnológico', 'informática'] ['performance', 'computing', 'platform', 'which', 'funded'] ['ivace', 'implemented', 'within'] ['space', 'being', 'these', 'experiments', 'tech4cv', 'project'] [] ['volume', '42381o', 'catalá', 'analysis', 'public', 'image', 'datasets']
# LSA Model
number_of_topics=100
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_law =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values
#These variables are for further experimentation in vertical comparission mantel test
model_science_paragraph=create_gensim_lsa_model(clean_text,number_of_topics,words)
dict(model_science.show_topic(0, topn=5))
{'covid': 0.8173316479466738,
'imag': 0.32835719547350367,
'model': 0.19244766283134151,
'detect': 0.1700824506894798,
'dataset': 0.13468398563227243}
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph25.png')
# LSA Model
a=pd.DataFrame()
for i in range(number_of_topics):
words_science =dict(model_science.show_topic(i, topn=words))
b=pd.DataFrame(words_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_science.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled28.png')
cosine-similarity
covid imag model detect dataset
covid 1 0.00123781 0.000365384 0.00800962 0.000934745
imag 0.00123781 1 0.000764231 -0.00799469 -0.00232223
model 0.000365384 0.000764231 1 0.00280805 -0.000605737
detect 0.00800962 -0.00799469 0.00280805 1 0.000416076
dataset 0.000934745 -0.00232223 -0.000605737 0.000416076 1
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *100 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig28.png')
document_list,titles,word_count_psy=load_data("","covid-psychology-15.txt")
Number of words in text file : 58236 Total Number of Documents: 6353
clean_text=preprocess_data(document_list)
['international', 'sociology'] [] ['author'] ['article', 'reuse', 'guidelines'] ['sagepub', 'journals', 'permissions'] ['0268580920948807'] ['journals', 'sagepub'] ['psychology', 'politics'] ['covid', 'misinfodemics'] ['people'] ['believe', 'misinfodemics'] ['sonia', 'mukhtar'] ['university', 'management', 'technology', 'lahore', 'pakistan'] ['abstract'] ['misinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adverse'] ['health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate'] ['evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects'] ['misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier'] ['about', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article'] ['review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects'] ['responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article'] ['shows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product'] ['psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive'] ['crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations'] ['psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications'] ['development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health'] ['keywords'] ['conspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'science'] ['mechanisms', 'misinfodemics'] ['modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surrounding'] ['corresponding', 'author'] ['sonia', 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['original', 'research'] ['published', 'september'] ['fpsyg', '01924'] ['edited'] ['ilhan', 'ozturk'] ['university', 'turkey'] ['reviewed'] ['jasim', 'tariq'] ['university', 'pakistan'] ['muhammad', 'usman'] ['jiangsu', 'university', 'china'] ['correspondence'] ['khurram', 'shehzad'] ['khurramscholar64', 'hotmail'] ['specialty', 'section'] ['article', 'submitted'] ['organizational', 'psychology'] ['section', 'journal'] ['frontiers', 'psychology'] ['received'] ['accepted'] ['published', 'september'] ['citation'] ['shehzad', 'xiaoxing'] ['rehman', 'ilyas'] ['investigating', 'psychology'] ['financial', 'markets', 'during', 'covid'] ['study'] ['european', 'markets'] ['front', 'psychol'] ['fpsyg', '01924'] ['investigating', 'psychology'] ['financial', 'markets', 'during', 'covid'] ['study'] ['european', 'markets'] ['khurram', 'shehzad1'] ['xiaoxing1'] ['muhammad', 'arif2'] ['khaliq', 'rehman3'] ['muhammad', 'ilyas2'] ['school', 'economics', 'management', 'southeast', 'university', 'nanjing', 'china', 'school', 'economics', 'finance'] ['jiaotong', 'university', 'china', 'school', 'management', 'wuhan', 'university', 'technology', 'wuhan', 'china'] ['novel', 'coronavirus', 'covid', 'imperatively', 'shaken', 'behavior', 'global'] ['financial', 'markets', 'study', 'estimated', 'impact', 'covid', 'behavior'] ['financial', 'markets', 'europe', 'results', 'revealed', 'returns'] ['index', 'greatly', 'affected', 'lockdown', 'owing', 'covid'] ['however', 'health', 'crisis', 'generated', 'novel', 'coronavirus', 'significantly', 'decreased'] ['stock', 'returns', 'nasdaq', 'composite', 'index', 'results', 'showed'] ['economic', 'crisis', 'generated', 'pandemic', 'spain', 'impact'] ['compared', 'health', 'crisis', 'itself', 'other', 'italy'] ['stock', 'markets', 'affected', 'health', 'crisis', 'contrasted', 'economic'] ['crisis', 'while', 'short', 'lockdown', 'conditions', 'economic', 'instability', 'lower'] ['stock', 'returns', 'stock', 'markets', 'witnessed', 'short'] ['deficiency', 'health', 'management', 'systems', 'imperatively', 'damaged', 'stock', 'returns'] ['london', 'stock', 'exchange', 'investigation', 'revealed', 'deficiency', 'health', 'systems'] ['lockdown', 'conditions', 'imperatively', 'damaged', 'structure', 'financial', 'markets'] ['inferring', 'sustainable', 'development', 'these', 'nations', 'covid'] ['study', 'suggested', 'governments', 'should', 'allocate', 'their', 'budget', 'health'] ['sector', 'overcome', 'health', 'crisis', 'future'] ['keywords', 'covid', 'financial', 'stability', 'financial', 'markets', 'psychology', 'sustainable', 'development', 'global'] ['development', 'health', 'crisis', 'economic', 'crisis'] ['introduction'] ['historically', 'countries', 'affected', 'pandemic', 'epidemic', 'large'] ['impact', 'within', 'economy', 'their', 'financial', 'markets', 'specific'] ['example', 'would', 'spread', 'ebola', 'disease', 'which', 'caused'] ['billion', 'dollars', 'fernandes', 'however', 'potential', 'damages', 'current'] ['virus', 'still', 'largely', 'unknown', 'significantly', 'coronavirus', 'covid', 'infection', 'disease'] ['first', 'reported', 'wuhan', 'december', 'spread', 'rapidly', 'almost'] ['whole', 'world', 'within', 'months', 'albulescu', 'compared', 'severe', 'acute'] ['respiratory', 'syndrome', 'covid', 'contagious', 'which', 'indicated'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['different', 'fatality', 'rate1'] ['covid', 'infected'] ['individuals', 'deaths', 'documented'] ['throughout', 'globe', 'until', 'april', 'financial', 'times'] ['order', 'eradicate', 'pandemic', 'world', 'health'] ['organization', 'recommended', 'maintaining', 'social', 'distance'] ['which', 'generated', 'severe', 'lockdown', 'situation', 'globe'] ['accordingly', 'economic', 'circle', 'whole'] ['world', 'disturbed', 'notably', 'online', 'travel'] ['agencies', 'airlines', 'hotels', 'unexpectedly', 'declined'] ['world', 'economic', 'forum', 'prices', 'nosedived'] ['sudden', 'outbreak', 'pandemic'] ['globalization', 'current', 'coronavirus', 'outbreak', 'aggravate'] ['economic', 'condition', 'which', 'toward', 'financial'] ['meltdown', 'huang', 'pandemic', 'caused'] ['severe', 'psychological', 'impact', 'economy', 'while', 'agitating'] ['service', 'industries', 'financial', 'markets'] ['moreover', 'coronavirus', 'outbreak', 'severely', 'affected'] ['financial', 'markets', 'while', 'declining', 'value', 'stock', 'index'] ['daube', 'surprisingly', 'specific', 'events'] ['fluctuate', 'stock', 'values', 'shehzad', 'sohail', 'figure'] ['revealed', 'market', 'value', 'european', 'american', 'chinese'] ['markets', 'january', 'march'] ['period', 'divided', 'three', 'rounds', 'comparison', 'purposes'] ['first', 'lagged', 'january', 'january'] ['second', 'lagged', 'january', 'march'] ['third', 'lagged', 'denoted', 'period', 'march', 'march'] ['results', 'showed', 'market', 'values', 'these'] ['indices', 'significantly', 'declined', 'third', 'lagged', 'covid', 'approached', 'western', 'countries', 'spain', 'stock', 'market'] ['share', 'value', 'decreased', 'third', 'lagged'] ['covid', 'tremendously', 'shrank', 'greece', 'stock', 'markets'] ['moreover', 'figure', 'illustrates', 'volatility', 'index'] ['january', 'march', 'denotes', 'world', 'faced'] ['severe', 'financial', 'crises', 'stock', 'markets', 'collapsed'] ['economies', 'through', 'enormous', 'pressure', 'similar'] ['scenario', 'built', 'during', 'global', 'pandemic', 'covid', 'stock', 'market', 'variance', 'highest', 'second'] ['after', 'global', 'financial', 'crises'] ['regard', 'novel', 'coronavirus', 'impact'] ['detrimental', 'pandemic', 'behavior', 'unknown', 'effect'] ['stock', 'return', 'longer', 'relevant'] ['stock', 'volatility', 'agitated', 'enormous', 'spread'] ['pandemic', 'resulting', 'severe', 'economic', 'crisis', 'sharif'] ['circumstances', 'quite', 'significant'] ['analyze', 'coronavirus', 'short', 'impacts'] ['advanced', 'countries', 'stock', 'return', 'investigation', 'argued'] ['confirmed', 'patients', 'novel', 'coronavirus', 'increase'] ['lockdown', 'conditions', 'becomes', 'stricter', 'which'] ['significant', 'economic', 'crisis', 'shocked', 'economic', 'stability'] ['nations', 'moreover', 'deaths', 'befell', 'result', 'novel'] ['coronavirus', 'specified', 'deficiency', 'health', 'facilities', 'which'] ['caused', 'substantial', 'health', 'crisis', 'particularly', 'examination'] ['analyzed', 'effects', 'economic', 'health', 'crisis'] ['novel', 'coronavirus', 'behavior', 'financial', 'markets'] ['fatality', 'while', 'fatality', 'novel', 'coronavirus'] ['around', 'globe'] ['united', 'states', 'germany', 'united', 'kingdom', 'italy'] ['spain', 'period', 'february', 'april'] ['study', 'elucidates', 'effects', 'executing', 'linear', 'autoregressive', 'distributed', 'lagged', 'nardl', 'model'] ['investigation', 'provides', 'remarkable', 'policies', 'handle'] ['impacts', 'covid', 'financial', 'market'] ['answers', 'momentous', 'queries', 'researchers', 'policymakers'] ['government', 'officials', 'academicians', 'firstly'] ['covid', 'linear', 'impact', 'financial', 'markets'] ['behavior', 'secondly', 'health', 'crisis', 'economic', 'instability'] ['generated', 'covid', 'significant', 'impact'] ['index', 'thirdly', 'nasdaq', 'composite', 'index'] ['index', 'respond', 'critically', 'covid', 'crisis'] ['fourthly', 'economic', 'crisis', 'generated', 'covid'] ['health', 'crisis', 'significant', 'impact', 'stock', 'returns'] ['fifthly', 'suspended', 'circle', 'economy'] ['lockdown', 'begin', 'again', 'according', 'author', 'knowledge'] ['first', 'study', 'examines', 'asymmetrical', 'impact'] ['covid', 'psychology', 'stock', 'markets', 'mostly'] ['infected', 'nations', 'world', 'firstly', 'project', 'ascertained'] ['stationary', 'level', 'study', 'variables', 'discovered'] ['variables', 'stationary', 'stationary'] ['hence', 'linear', 'version', 'autoregressive'] ['distributed', 'model', 'applied'] ['methodology'] [] ['study', 'utilized', 'daily', 'number', 'confirmed'] ['patients', 'deaths', 'covid', 'stock', 'markets'] ['spain', 'italy', 'germany', 'period'] ['february', 'april', 'study', 'taken'] ['database', 'yahoo', 'finance', 'european', 'center'] ['disease', 'control', 'prevention', 'further', 'investigation'] ['analyzed', 'daily', 'returns'] ['london', 'stock', 'exchange', 'nomination', 'spain', 'italy'] ['germany', 'however', 'nasdaq', 'composite'] ['index', 'represents'] ['methodology'] ['investigation', 'utilized', 'stock', 'market', 'daily', 'returns'] ['dependent', 'variable', 'confirmed', 'cases', 'deaths'] ['independent', 'variable', 'daily', 'returns', 'study'] ['computed', 'follows', 'syllignakis', 'kouretas', 'shehzad'] ['sohail'] [] [] [] ['denote', 'return', 'value', 'closing'] ['price', 'previous', 'closing', 'price'] ['stock', 'market', 'respectively', 'linear', 'association', 'between', 'these'] ['variables', 'defined'] ['1sprt', 'β1usct', 'β2usdt'] ['1nsrt', 'β1usct', 'β2usdt'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['figure', 'deterioration', 'stock', 'markets', 'owing', 'novel', 'coronavirus'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['figure', 'variations', 'global', 'financial', 'crisis'] ['covid', 'crisis'] ['1ibert', 'β1spct', 'β2spdt'] ['1ftmrt', 'β1itlct', 'β2itldt'] ['1daxrt', 'β1gerct', 'β2gerdt'] ['1lsert', 'β1ukct', 'β2ukdt'] ['where', 'signifies', 'first', 'difference', 'β0β1β2'] ['independent', 'parameters', 'however'] ['defines', 'return', 'values', 'nasdaq'] ['composite', 'index'] ['london', 'stock', 'exchange', 'respectively', 'moreover'] ['indicate', 'confirmed', 'cases', 'covid', 'spain', 'italy', 'germany', 'respectively'] ['further', 'refer', 'confirmed'] ['deaths', 'covid', 'spain', 'italy', 'germany'] ['respectively'] ['ascertain', 'short', 'asymmetries'] ['study', 'employed', 'linear', 'autoregressive', 'distributed'] ['nardl', 'model', 'introduced', 'model'] ['performs', 'small', 'number', 'observations'] ['applied', 'mixed', 'level', 'stationary'] ['evaluates', 'linearity', 'cointegration', 'between', 'variables'] ['equation', 'shared', 'short', 'variations'] ['asymmetries', 'after', 'taking', 'parameters', 'account'] ['nardl', 'model', 'extended'] ['pesaran', 'model', 'hence'] ['nardl', 'model', 'stock', 'market', 'specified'] ['1sprt'] [] [] [] ['1sprt'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['γ2ausc'] ['γ2busc'] ['γ3ausd'] [] [] [] ['1nsrt'] [] [] [] ['1nsrt'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['γ1nsrt', 'γ2ausc'] ['γ2busc'] [] [] ['γ3busd'] [] ['1ibert'] [] [] [] ['1ibert'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['γ1ibert', 'γ2aspc'] ['γ2bspc'] [] [] ['γ3bspd'] [] ['1ftmrt'] [] [] [] ['1ftmrt'] [] [] [] ['1itlc'] [] [] [] [] [] ['1itlc'] [] [] [] [] [] ['1itld'] [] [] [] [] [] [] ['γ1ftmrt', 'γ2aitlc'] [] [] [] [] [] [] [] ['1daxrt'] [] [] [] ['1daxrt'] [] [] [] ['1gerc'] [] [] [] [] [] ['1gerc'] [] [] [] [] ['1gerd'] [] [] [] [] [] [] ['γ1daxrt', 'γ2agerc'] ['γ2bgerc'] [] [] ['γ3bgerd'] [] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['1lsert'] [] [] [] ['1lsert'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['γ1lsert', 'γ2aukc'] ['γ2bukc'] [] [] ['γ3bukd'] [] ['ϑ2bϑ3aand', 'short', 'factors', 'while'] [] [] ['indicates', 'parameters'] ['whereas', 'symbolizes', 'number', 'model'] ['based', 'akaik', 'information', 'criterion', 'schwarz', 'information'] ['criterion', 'above', 'stated', 'equations', 'assumed'] ['confirmed', 'number', 'cases', 'deaths', 'covid'] ['asymmetrical', 'impact', 'stock', 'returns'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['designates', 'positive', 'shock', 'though'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['implies', 'negative', 'shock', 'variable'] ['computed', 'follows'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['delineates', 'positive', 'shock', 'variable', 'denotes'] ['asymmetric', 'distributive', 'error', 'correction'] ['these', 'factors', 'written', 'follows'] ['1sprt'] [] [] [] ['1sprt'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['φ1ectt'] ['1nsrt'] [] [] [] ['1nsrt'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['φ1ectt'] ['1ibert'] [] [] [] ['1ibert'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['φ1ectt'] ['1ftmrt'] [] [] [] ['1ftmrt'] [] [] [] ['1itlc'] [] [] [] [] [] ['1itlc'] [] [] [] [] [] ['1itld'] [] [] [] [] [] [] ['φ1ectt'] ['1daxrt'] [] [] [] ['1daxrt'] [] [] [] ['1gerc'] [] [] [] [] [] ['1gerc'] [] [] [] [] [] ['1gerd'] [] [] [] [] [] [] ['φ1ectt'] ['1lsert'] [] [] [] ['1lsert'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['φ1ectt'] ['directs', 'error', 'correction'] ['cointegration', 'among', 'variables', 'examined', 'through', 'bound'] ['approach', 'pesaran', 'method', 'relies', 'evaluate', 'hypothesis'] ['purpose', 'pesaran', 'defined', 'bounds'] ['upper', 'lower', 'bound', 'estimated', 'statistics'] ['higher', 'upper', 'bound', 'limit', 'hypothesis'] ['cointegration', 'rejected', 'however', 'projected', 'value', 'lower', 'lower', 'bound', 'limit', 'hypothesis', 'cannot'] ['rejected', 'besides', 'value', 'remains', 'between'] ['limits', 'results', 'conclusive', 'project'] ['employed', 'verify', 'asymmetric'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['andshort'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['asymmetric'] ['relationship', 'between', 'study', 'variables'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['study', 'findings'] ['descriptive', 'summary', 'showed', 'table', 'during', 'covid', 'stock', 'returns', 'negative', 'further', 'skewness'] ['values', 'these', 'markets', 'negative', 'except'] ['kurtosis', 'which', 'predicts', 'chances', 'these'] ['table', 'presents', 'results', 'augmented', 'dickey', 'fuller'] ['dickey', 'fuller', 'phillip', 'perron'] ['phillips', 'perron', 'showed', 'study', 'variables'] ['diverse', 'stationary', 'levels', 'variable'] ['cointegrated'] ['bound', 'cointegration', 'fallouts'] ['outcomes', 'bound', 'displayed', 'table', 'disclosed'] ['significant', 'statistical', 'evidence', 'association', 'between'] ['study', 'variables', 'equation', 'statistics', 'joint'] ['significance', 'lagged', 'level', 'parameters', 'stated'] ['respectively', 'these', 'values'] ['surpass', 'upper', 'bound', 'limits', 'implying'] ['cointegration', 'exists', 'among', 'economic', 'crisis', 'health'] ['crisis', 'financial', 'markets', 'equation'] ['short', 'asymmetric'] ['cointegration', 'reckoning'] ['table', 'exhibited', 'upshots', 'statistics'] ['particularized', 'linear', 'association'] ['however', 'revealed', 'short', 'asymmetric'] ['affiliation', 'besides', 'caused', 'asymmetric'] ['influence', 'short', 'periods'] ['while', 'possessed', 'short', 'asymmetry', 'additionally'] ['table', 'descriptive', 'statistics'] [] ['41737', '08997', '70833', '28187'] ['skewness', '68003', '687765', '641313', '027343', '392073', '081854', '35422', '186996'] ['kurtosis', '36907', '166164', '206693', '903565', '778953', '75376', '380136', '389965'] ['jarque', '26244', '69524', '87966', '47298', '76708'] ['probability', '00434', '000161'] [] ['18523', '54717', '42086', '40245'] ['skewness', '927378', '53985', '120857', '230936', '65869', '232889', '058817', '31595'] ['kurtosis', '43888', '59924', '573906', '120135', '135106', '868864', '472667', '04434'] ['jarque', '44518', '49843', '45175', '59309', '03773', '08892'] ['probability', '000013', '003185', '000121', '000872'] ['source', 'author', 'calculation'] ['table'] ['statistics', 'statistics'] ['variable', 'level', 'level'] ['893713', '54426', '057616'] ['293963', '969976', '971366', '51473'] ['935247', '359161', '147762', '393867'] ['631659', '55646', '451458'] ['127702', '174362', '400067', '35456'] ['300231', '849862', '488626', '372171'] ['27056', '985616', '446574', '65801'] ['506727', '32241', '159422', '09496'] ['552953', '028486', '374729', '97616'] ['775255', '591517', '768878', '87539'] ['816325', '578091', '746256', '61778'] ['429147', '36282', '571441', '31254'] ['89506', '022076', '13962'] ['847753', '567572', '310638', '46537'] ['138569', '097646', '384682', '006879'] ['66594', '40744', '64235', '28961'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['table', 'results', 'bound'] ['statistic', 'value', 'signif', 'statistics', 'value', 'signif'] [] ['statistic', '02410', 'statistic', '165881'] [] [] [] ['statistic', '11238', 'statistic', '23325'] [] [] [] ['statistic', '490181', 'statistic', '03259'] [] [] ['source', 'author', 'calculation'] ['table'] ['asymmetry', 'short', 'asymmetry', 'asymmetry', 'short', 'asymmetry'] [] ['242605'] [] [] ['574046'] ['589403', '609162', '338933'] [] ['569179', '725822', '640751'] ['61007', '786146', '718125'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['results', 'designated', 'asymmetric'] ['relationship', 'statistics', 'enlightened'] ['short', 'linear', 'impacts'] ['short', 'linear', 'affiliation'] ['moreover', 'denotes', 'asymmetric', 'liaison'] ['hence', 'existence', 'linearity', 'study', 'variables'] ['choose', 'nardl', 'model', 'computing'] ['short', 'factors'] ['modeling', 'nardl', 'parameters'] ['linear', 'fallouts', 'presented', 'table', 'illustrated'] ['expansion', 'contraction', 'brings', 'reduction'] ['increase', 'respectively', 'indicates', 'strict', 'lockdown'] ['substantially', 'decreases', 'market', 'returns'] ['versa', 'further', 'positive', 'shock', 'caused', 'significant'] ['short', 'lagged', 'values', 'specified'] ['negative', 'shock', 'amplified', 'besides', 'lagged'] ['terms', 'described', 'uptick', 'effect', 'improved'] ['negative', 'shock', 'second', 'augmented'] ['first', 'third', 'represents', 'reverse', 'impact'] ['furthermore', 'negative', 'significant', 'value', 'stated'] ['disequilibrium', 'occurred', 'today', 'covid'] ['adjust', 'speed', 'units', 'subsequent'] ['squared', 'value', 'indicated', 'volatility'] ['owing', 'covid'] ['model', 'results', 'exhibited', 'table', 'particularized'] ['positive', 'negative', 'shocks', 'escalate'] ['while', 'enlargement', 'reduction', 'instigate', 'decrease'] ['increase', 'specifies', 'upsurge', 'health', 'crisis'] ['expressively', 'distressed', 'nasdaq', 'composite', 'index'] ['versa', 'nonetheless', 'short', 'negative', 'shock'] ['demonstrated', 'imperative', 'decrease'] ['whereas', 'expansion', 'lessens', 'negative'] ['significant', 'parameter', 'stated', 'financial', 'instability'] ['generated', 'covid', 'would', 'settled', 'speed'] ['units', 'square', 'value', 'signified'] ['instability', 'occurring', 'result', 'covid'] ['finding', 'parameters', 'model', 'table', 'identified'] ['assertive', 'shock', 'encouraging', 'effect'] ['besides', 'adverse', 'shocks', 'remain', 'insignificant', 'other'] ['escalation', 'decline', 'negative', 'positive', 'influence'] ['implying', 'strict', 'lockdown', 'considerably', 'caused'] ['financial', 'crisis', 'spain', 'period', 'short'] ['period', 'intensification', 'decline'] ['increased', 'negative', 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'adherence', 'shows', 'practical', 'rather'] ['psychological', 'people', 'trains', 'because'] ['needed', 'money', 'survive', 'government', 'support', 'schemes', 'insufficient', 'people'] ['could', 'exercise', 'those', 'urban', 'areas', 'limited', 'public', 'space'] ['employers', 'failed', 'provide', 'support', 'social', 'distancing', 'hygiene'] ['those', 'income', 'wealth', 'crowded', 'homes'] ['outcomes', 'these', 'systematic', 'inequalities', 'predictable', 'poorer', 'people'] ['repeatedly', 'shown', 'vulnerable', 'infection', 'likely', 'these'] ['inequalities', 'persisted', 'second', 'phase', 'lockdown'] ['lower', 'income', 'people', 'being', 'likely'] ['bring', 'contact', 'others'] ['finally', 'there', 'evidence', 'specific', 'mismanagement', 'failure'] ['communication', 'response', 'thing', 'observed', 'changed', 'which'] ['might', 'therefore', 'indicate', 'recognition', 'earlier', 'error', 'public'] ['addressed', 'official', 'messaging', 'initial', 'government', 'communications', 'stressed'] ['oneself', 'individual', 'example'] ['current', 'advice', 'important', 'thing', 'individuals', 'protect', 'themselves'] ['remains', 'washing', 'their', 'hands', 'often', 'least', 'seconds', 'water'] ['emphasis', 'added'] ['message', 'people', 'therefore', 'picked', 'about'] ['themselves', 'personally', 'individual', 'focused', 'messaging', 'people', 'discount'] ['especially', 'consider', 'themselves', 'young', 'healthy', 'later', 'there', 'shift'] ['rationale', 'being', 'protect', 'protect', 'others', 'change'] ['potential', 'victim', 'spreader', 'which', 'seems'] ['persuasive'] ['collective', 'psychology'] ['example', 'makes', 'point', 'indeed', 'psychology', 'heavily', 'involved', 'public'] ['response', 'covid', 'however', 'psychology', 'fixed', 'behavioural', 'tendencies'] ['since', 'hence', 'interest', 'motivations', 'boundaries', 'concern'] ['varies', 'contextual', 'factors', 'political', 'leadership', 'which', 'failed', 'initially'] ['communicate', 'collectivist', 'terms', 'course', 'psychology', 'matters', 'happens'] ['emergencies', 'reasons', 'other', 'inevitable', 'collective', 'selfishness', 'thoughtlessness'] ['reaction'] ['consider', 'first', 'conditions', 'under', 'which', 'behaviour', 'competitive'] ['cooperative', 'emergencies', 'there', 'reports', 'mutual', 'social', 'support'] ['members', 'public', 'during', 'covid', 'crisis', 'reviews', 'suggest', 'cooperation'] ['among', 'survivors', 'common', 'emergencies', 'members', 'public'] ['lives', 'professional', 'responders', 'drury', 'emergencies'] ['people', 'compete', 'trample', 'other', 'conditions'] ['occur', 'chertkoff', 'kushigian', 'comparison', 'different', 'evacuations', 'found'] ['there', 'competition', 'exits', 'narrow', 'unfamiliar'] ['people', 'compete', 'coordinate', 'evacuations', 'positioned'] ['psychologically', 'individuals', 'rather', 'group', 'members', 'mintz', 'shows'] ['evacuating', 'crowd', 'blocks', 'explained', 'terms'] ['prevalence', 'individual', 'competition', 'collective', 'setting', 'rather', 'terms'] ['excessive', 'emotion', 'these', 'cases', 'emergency', 'badly', 'absence'] ['collective', 'psychology', 'coordination', 'cooperation'] ['cooperating', 'giving', 'support', 'carry', 'risks', 'which', 'acknowledged'] ['evacuations', 'larger', 'group', 'slower', 'egress', 'because', 'speed', 'reduced'] ['through', 'people', 'interacting', 'other', 'aguirre', 'motivation'] ['support', 'other', 'survivors', 'risks', 'their', 'personal', 'safety'] ['collective', 'emergency', 'always', 'particular', 'individuals'] ['drury', 'cocking', 'reicher', 'covid', 'individual'] ['supporting', 'group', 'clear', 'where', 'supportive', 'behaviour', 'involves', 'physical'] ['proximity', 'whether', 'delivering', 'giving', 'emotional', 'support'] ['about', 'public', 'under', 'reaction', 'occur', 'under', 'estimation'] ['sometimes', 'characterized', 'optimistic', 'kinsey', 'gwynne', 'kuligowski'] ['kinateder', 'context', 'where', 'emergency', 'events'] ['reasonable', 'assume', 'happen', 'assumption', 'reverse'] ['emergency', 'events', 'become', 'common', 'example', 'after', 'spate'] ['terrorist', 'attacks', 'london', 'hundreds', 'people', 'oxford', 'street', 'noise'] ['turned', 'harmless', 'general', 'extent', 'which', 'information', 'concerning'] ['threat', 'plausible', 'function', 'broad', 'social', 'context', 'dangers', 'expectations'] ['danger', 'raised', 'readiness', 'other', 'action', 'greater', 'context'] ['recent', 'incidents', 'relevant', 'social', 'group'] ['perceptions', 'become', 'collective', 'people', 'respond', 'direct'] ['signals', 'other', 'people', 'responses', 'signal', 'bruder', 'fischer', 'manstead'] ['suggest', 'extent', 'which', 'response', 'others', 'possible', 'threat'] ['conveying', 'information', 'dependent', 'relevance', 'these', 'others'] ['particular', 'context', 'which', 'often', 'function', 'shared', 'identity', 'based'] ['about', 'social', 'influence', 'processes', 'other', 'contexts', 'bruder'] ['covid', 'plausible', 'suppose', 'sight', 'others', 'community', 'routinely'] ['observing', 'ignoring', 'social', 'distancing', 'regulations', 'example', 'likely', 'strong'] ['signal', 'around', 'safety', 'doing', 'particularly', 'where', 'identify'] ['community', 'these', 'exemplars', 'prototypes'] ['psychological', 'factors', 'interact', 'management', 'failures', 'explain'] ['emergency', 'events', 'badly', 'fearing', 'public', 'panic', 'leads', 'authorities', 'withhold'] ['information', 'about', 'emergency', 'drury', 'information'] ['emergency', 'increases', 'public', 'anxiety', 'public', 'perceives', 'information'] ['being', 'withheld', 'damages', 'their', 'relationship', 'authority', 'carter'] ['consequently', 'authorities', 'release', 'correct', 'information'] ['public', 'mistrust', 'covid', 'treat'] ['public', 'respect', 'order', 'build', 'trust', 'advice', 'given', 'behavioural'] ['scientists', 'government'] ['discussion', 'conclusions'] ['emergencies', 'people', 'behave', 'selfishly', 'thoughtlessly'] ['react', 'indeed', 'explained', 'research', 'suggests', 'conditions'] ['competition', 'prevail', 'cooperation', 'questioning', 'notion'] ['public', 'reactions', 'default', 'major', 'cause', 'problems', 'covid'] ['crisis', 'existing', 'literature', 'disasters', 'support', 'prima', 'facie'] ['major', 'problems', 'covid', 'response', 'outcomes', 'better', 'understood'] ['otherwise', 'terms', 'political', 'under', 'reaction', 'systemic', 'issues', 'mismanagement'] ['collective', 'panic', 'referred', 'disaster', 'literature', 'disasters', 'drury'] ['rather', 'neutral', 'description', 'people', 'actually', 'behave'] ['understood', 'particular', 'discourse', 'cultural', 'representation', 'which'] ['psychologizes', 'indeed', 'pathologizes', 'public', 'responses', 'emergencies', 'disasters'] ['given', 'known', 'about', 'under', 'reaction', 'systemic', 'factors', 'mismanagement'] ['emergencies', 'emphasize', 'instead', 'collective', 'behaviour', 'clear'] ['ideological', 'functions', 'naturalizing', 'fatalities', 'distracts', 'causes'] ['might', 'responsible', 'mismanagement', 'instead', 'blaming', 'victims'] ['irony', 'course', 'being', 'problem', 'collective', 'psychology'] ['emergencies', 'solidarity', 'cooperation', 'commonly', 'witnessed', 'among', 'community'] ['members', 'strangers', 'usually', 'solution', 'collective', 'psychology', 'therefore'] ['should', 'harnessed', 'effectively', 'policy', 'practice', 'covid', 'response'] ['elcheroth', 'drury', 'through', 'framing', 'threat', 'solution', 'collective'] ['terms', 'through', 'emphasizing', 'shared', 'norms', 'around', 'collective', 'being', 'safety'] ['drury', 'people', 'emergencies', 'probably', 'because'] ['collective', 'psychology']
# LSA Model
number_of_topics=100
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values
#These variables are for further experimentation in vertical comparission mantel test
model_psy_paragraph=create_gensim_lsa_model(clean_text,number_of_topics,words)
dict(model_psy.show_topic(0, topn=5))
{'covid': 0.3665193827288808,
'pandem': 0.36094244989669616,
'health': 0.3248658297265896,
'polici': 0.29428572192418073,
'impact': 0.23191430022007598}
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph26.png')
# LSA Model
a=pd.DataFrame()
for i in range(number_of_topics):
words_psy =dict(model_psy.show_topic(i, topn=words))
b=pd.DataFrame(words_psy,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_psy.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled29.png')
cosine-similarity
covid pandem health polici impact
covid 1 -0.000251484 -0.00102935 0.00200395 0.00491427
pandem -0.000251484 1 -0.00206637 -0.000776059 0.00266831
health -0.00102935 -0.00206637 1 0.00450039 0.000610171
polici 0.00200395 -0.000776059 0.00450039 1 -0.00567595
impact 0.00491427 0.00266831 0.000610171 -0.00567595 1
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *100 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig29.png')
mantel_test(5)
Pairwise-distance
covid pandem health polici impact imag \
covid 2.10734e-08 1.29174 1.34614 0.996639 0.997331 1.40455
pandem 1.29174 0 1.22761 0.831439 0.863993 1.30309
health 1.34614 1.22761 0 0.89882 0.846913 1.34228
polici 0.996639 0.831439 0.89882 0 0.130483 0.994958
impact 0.997331 0.863993 0.846913 0.130483 0 1.00193
imag 1.40455 1.30309 1.34228 0.994958 1.00193 0
model 1.40535 1.30616 1.34301 0.994937 1.00125 1.40434
detect 1.37031 1.27076 1.30048 0.953255 0.95357 1.38056
dataset 1.4048 1.30695 1.34452 0.994289 1.00036 1.40636
model detect dataset
covid 1.40535 1.37031 1.4048
pandem 1.30616 1.27076 1.30695
health 1.34301 1.30048 1.34452
polici 0.994937 0.953255 0.994289
impact 1.00125 0.95357 1.00036
imag 1.40434 1.38056 1.40636
model 0 1.37334 1.40534
detect 1.37334 1.49012e-08 1.37484
dataset 1.40534 1.37484 0
Pairwise-distance
covid pandem health polici impact imag \
covid 2.10734e-08 1.40413 1.39789 1.18336 1.39399 0.996695
pandem 1.40413 0 1.39525 1.1809 1.39221 0.992328
health 1.39789 1.39525 0 1.16999 1.38682 0.982234
polici 1.18336 1.1809 1.16999 1.49012e-08 1.17576 0.647287
impact 1.39399 1.39221 1.38682 1.17576 0 0.982498
imag 0.996695 0.992328 0.982234 0.647287 0.982498 0
model 1.04603 1.03991 1.02912 0.786791 1.03451 0.342652
detect 0.995885 0.990851 0.981511 0.642914 0.981984 0.0495758
dataset 0.995199 0.99045 0.980643 0.641984 0.98134 0.0456046
model detect dataset
covid 1.04603 0.995885 0.995199
pandem 1.03991 0.990851 0.99045
health 1.02912 0.981511 0.980643
polici 0.786791 0.642914 0.641984
impact 1.03451 0.981984 0.98134
imag 0.342652 0.0495758 0.0456046
model 9.12506e-09 0.338194 0.338994
detect 0.338194 4.65661e-10 0.0309682
dataset 0.338994 0.0309682 0
(-0.36722959799295146, 0.8156, -1.0916996508045393)
mantel_test(10)
Pairwise-distance
covid pandem health polici impact countri \
covid 2.10734e-08 1.29174 1.34614 0.996639 0.997331 1.0006
pandem 1.29174 0 1.22761 0.831439 0.863993 0.8195
health 1.34614 1.22761 0 0.89882 0.846913 0.909924
polici 0.996639 0.831439 0.89882 0 0.130483 0.0886125
impact 0.997331 0.863993 0.846913 0.130483 0 0.158511
countri 1.0006 0.8195 0.909924 0.0886125 0.158511 2.63418e-09
develop 1.01972 0.836573 0.945239 0.219142 0.258953 0.198135
nation 1.05631 0.956099 0.890934 0.351072 0.338373 0.367674
sustain 0.994409 0.845449 0.903259 0.042095 0.121871 0.0918988
economi 0.995097 0.834319 0.891971 0.0466139 0.115609 0.0887288
imag 1.40455 1.30309 1.34228 0.994958 1.00193 1.00063
model 1.40535 1.30616 1.34301 0.994937 1.00125 0.998605
detect 1.37031 1.27076 1.30048 0.953255 0.95357 0.953814
dataset 1.4048 1.30695 1.34452 0.994289 1.00036 0.999229
learn 1.39805 1.29449 1.33085 0.981304 0.971631 0.989446
use 1.40326 1.3016 1.33906 0.988543 1.00225 0.997627
class 1.40046 1.30392 1.33892 0.989182 0.993974 0.990393
base 1.39188 1.2854 1.32855 0.975671 0.985234 0.979056
chest 1.35157 1.22446 1.27388 0.907043 0.910788 0.891392
develop nation sustain economi imag model \
covid 1.01972 1.05631 0.994409 0.995097 1.40455 1.40535
pandem 0.836573 0.956099 0.845449 0.834319 1.30309 1.30616
health 0.945239 0.890934 0.903259 0.891971 1.34228 1.34301
polici 0.219142 0.351072 0.042095 0.0466139 0.994958 0.994937
impact 0.258953 0.338373 0.121871 0.115609 1.00193 1.00125
countri 0.198135 0.367674 0.0918988 0.0887288 1.00063 0.998605
develop 0 0.41009 0.218511 0.220412 1.02046 1.0164
nation 0.41009 0 0.349667 0.334864 1.05385 1.05152
sustain 0.218511 0.349667 0 0.0313482 0.993415 0.993578
economi 0.220412 0.334864 0.0313482 0 0.994138 0.994042
imag 1.02046 1.05385 0.993415 0.994138 0 1.40434
model 1.0164 1.05152 0.993578 0.994042 1.40434 0
detect 0.968051 1.0135 0.950806 0.946972 1.38056 1.37334
dataset 1.01545 1.04781 0.993686 0.993929 1.40636 1.40534
learn 1.0095 1.05018 0.981862 0.983133 1.39648 1.39551
use 1.01089 1.05667 0.990938 0.991925 1.40281 1.40311
class 1.01348 1.04952 0.987859 0.988145 1.40137 1.40122
base 0.988448 1.02696 0.973888 0.97458 1.39307 1.39184
chest 0.883996 0.97164 0.904441 0.903399 1.33931 1.34455
detect dataset learn use class base \
covid 1.37031 1.4048 1.39805 1.40326 1.40046 1.39188
pandem 1.27076 1.30695 1.29449 1.3016 1.30392 1.2854
health 1.30048 1.34452 1.33085 1.33906 1.33892 1.32855
polici 0.953255 0.994289 0.981304 0.988543 0.989182 0.975671
impact 0.95357 1.00036 0.971631 1.00225 0.993974 0.985234
countri 0.953814 0.999229 0.989446 0.997627 0.990393 0.979056
develop 0.968051 1.01545 1.0095 1.01089 1.01348 0.988448
nation 1.0135 1.04781 1.05018 1.05667 1.04952 1.02696
sustain 0.950806 0.993686 0.981862 0.990938 0.987859 0.973888
economi 0.946972 0.993929 0.983133 0.991925 0.988145 0.97458
imag 1.38056 1.40636 1.39648 1.40281 1.40137 1.39307
model 1.37334 1.40534 1.39551 1.40311 1.40122 1.39184
detect 1.49012e-08 1.37484 1.37019 1.3719 1.37034 1.3651
dataset 1.37484 0 1.39613 1.40298 1.40176 1.39208
learn 1.37019 1.39613 0 1.39579 1.39156 1.38103
use 1.3719 1.40298 1.39579 1.49012e-08 1.40056 1.39178
class 1.37034 1.40176 1.39156 1.40056 0 1.39185
base 1.3651 1.39208 1.38103 1.39178 1.39185 0
chest 1.31912 1.33768 1.32938 1.33524 1.33583 1.32553
chest
covid 1.35157
pandem 1.22446
health 1.27388
polici 0.907043
impact 0.910788
countri 0.891392
develop 0.883996
nation 0.97164
sustain 0.904441
economi 0.903399
imag 1.33931
model 1.34455
detect 1.31912
dataset 1.33768
learn 1.32938
use 1.33524
class 1.33583
base 1.32553
chest 0
Pairwise-distance
covid pandem health polici impact countri \
covid 2.10734e-08 1.40413 1.39789 1.18336 1.39399 1.39432
pandem 1.40413 0 1.39525 1.1809 1.39221 1.39094
health 1.39789 1.39525 0 1.16999 1.38682 1.38454
polici 1.18336 1.1809 1.16999 1.49012e-08 1.17576 1.17537
impact 1.39399 1.39221 1.38682 1.17576 0 1.38651
countri 1.39432 1.39094 1.38454 1.17537 1.38651 0
develop 1.38489 1.38515 1.37755 1.16731 1.37591 1.36555
nation 1.21755 1.21857 1.21016 0.823131 1.21174 1.21769
sustain 1.36573 1.35662 1.34834 1.14091 1.35234 1.35677
economi 1.37203 1.36941 1.36029 1.15818 1.35861 1.36218
imag 0.996695 0.992328 0.982234 0.647287 0.982498 0.977732
model 1.04603 1.03991 1.02912 0.786791 1.03451 1.03679
detect 0.995885 0.990851 0.981511 0.642914 0.981984 0.977962
dataset 0.995199 0.99045 0.980643 0.641984 0.98134 0.977426
learn 1.00215 1.00095 0.998503 0.623397 1.00042 0.993506
use 1.02389 1.02576 1.01012 0.675423 1.01065 1.01282
class 0.996184 0.99145 0.981399 0.642767 0.982192 0.979189
base 1.32217 1.3162 1.31425 1.10065 1.31409 1.3065
chest 0 0 0 0 0 0
develop nation sustain economi imag model \
covid 1.38489 1.21755 1.36573 1.37203 0.996695 1.04603
pandem 1.38515 1.21857 1.35662 1.36941 0.992328 1.03991
health 1.37755 1.21016 1.34834 1.36029 0.982234 1.02912
polici 1.16731 0.823131 1.14091 1.15818 0.647287 0.786791
impact 1.37591 1.21174 1.35234 1.35861 0.982498 1.03451
countri 1.36555 1.21769 1.35677 1.36218 0.977732 1.03679
develop 0 1.13752 1.32203 1.34497 0.965994 1.00406
nation 1.13752 0 1.17528 1.23061 0.69882 0.754526
sustain 1.32203 1.17528 2.10734e-08 1.33354 0.935113 1.00733
economi 1.34497 1.23061 1.33354 1.49012e-08 0.942429 0.995113
imag 0.965994 0.69882 0.935113 0.942429 0 0.342652
model 1.00406 0.754526 1.00733 0.995113 0.342652 9.12506e-09
detect 0.963716 0.705588 0.934026 0.943407 0.0495758 0.338194
dataset 0.963882 0.702796 0.933404 0.943422 0.0456046 0.338994
learn 0.974276 0.751772 0.960399 0.958062 0.166589 0.371646
use 1.0021 0.795146 0.942551 0.970375 0.25469 0.427486
class 0.966376 0.702752 0.934681 0.94393 0.0609852 0.342388
base 1.30421 1.12283 1.2633 1.28301 0.862478 0.835972
chest 0 0 0 0 0 0
detect dataset learn use class base \
covid 0.995885 0.995199 1.00215 1.02389 0.996184 1.32217
pandem 0.990851 0.99045 1.00095 1.02576 0.99145 1.3162
health 0.981511 0.980643 0.998503 1.01012 0.981399 1.31425
polici 0.642914 0.641984 0.623397 0.675423 0.642767 1.10065
impact 0.981984 0.98134 1.00042 1.01065 0.982192 1.31409
countri 0.977962 0.977426 0.993506 1.01282 0.979189 1.3065
develop 0.963716 0.963882 0.974276 1.0021 0.966376 1.30421
nation 0.705588 0.702796 0.751772 0.795146 0.702752 1.12283
sustain 0.934026 0.933404 0.960399 0.942551 0.934681 1.2633
economi 0.943407 0.943422 0.958062 0.970375 0.94393 1.28301
imag 0.0495758 0.0456046 0.166589 0.25469 0.0609852 0.862478
model 0.338194 0.338994 0.371646 0.427486 0.342388 0.835972
detect 4.65661e-10 0.0309682 0.15534 0.249335 0.0473592 0.852356
dataset 0.0309682 0 0.153312 0.247878 0.0345772 0.867173
learn 0.15534 0.153312 0 0.262548 0.155206 0.873058
use 0.249335 0.247878 0.262548 5.26836e-09 0.241858 0.928796
class 0.0473592 0.0345772 0.155206 0.241858 0 0.868939
base 0.852356 0.867173 0.873058 0.928796 0.868939 0
chest 0 0 0 0 0 0
chest
covid 0
pandem 0
health 0
polici 0
impact 0
countri 0
develop 0
nation 0
sustain 0
economi 0
imag 0
model 0
detect 0
dataset 0
learn 0
use 0
class 0
base 0
chest 0
(-0.4775879754844936, 0.9934, -2.174520016647468)
def mantel_test_verticle(model_a,model_b,n):
words_science = dict(model_a.show_topic(0, topn=word_count_science))
words_psy = dict(model_b.show_topic(0, topn=word_count_psy))
top_words_science = dict(model_a.show_topic(0, topn=n))
top_words_psy = dict(model_b.show_topic(0,topn=n))
c={} #law words in non law
for key in top_words_science:
try:
c[key] = words_psy[key]
except KeyError:
c[key] = 0
top_words_psy.update(c)
b={} #non law words in law
for key in top_words_psy:
try:
b[key] = words_science[key]
except KeyError:
b[key] = 0
b.update(top_words_science)
top_words_science=b
#pairwise distance for law
a=pd.DataFrame()
for i in range(number_of_topics):
words_from_science =dict(model_a.show_topic(i, topn=word_count_science))
b=pd.DataFrame(words_from_science,index=[i])
a=a.append(b)
a=a.transpose()
df = pd.DataFrame(columns=list(top_words_science.keys()),
index = list(top_words_science.keys()))
l = list(top_words_science.keys())
print('Pairwise-distance')
for i in l:
for j in l:
try:
matrix=np.array([a.loc[i],a.loc[j]])
except KeyError:
matrix=np.zeros((2,3))
n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
df.loc[[i],[j]] = n[0,1]
print(df)
df1 = df.to_numpy()
#pairwise distace for non-law
a=pd.DataFrame()
for i in range(number_of_topics):
words_from_psy =dict(model_b.show_topic(i, topn=word_count_psy))
b=pd.DataFrame(words_from_psy,index=[i])
a=a.append(b)
a=a.transpose()
df = pd.DataFrame(columns=list(top_words_psy.keys()),
index = list(top_words_psy.keys()))
l = list(top_words_psy.keys())
print('Pairwise-distance')
for i in l:
for j in l:
try:
matrix=np.array([a.loc[i],a.loc[j]])
except KeyError:
matrix=np.zeros((2,3))
n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
df.loc[[i],[j]] = n[0,1]
print(df)
df2 = df.to_numpy()
dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])
dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])
return mantel.test(dist1, dist2, perms=10000, method='pearson', tail='upper')
mantel_test_verticle(model_science_articles,model_science_p,5)
Pairwise-distance
support granada covid programm research imag \
support 0 0.168942 1.53014 0.161791 0.26492 1.91808
granada 0.168942 0 1.59104 0.0865721 0.263193 1.90158
covid 1.53014 1.59104 0 1.60946 1.53741 2.33945
programm 0.161791 0.0865721 1.60946 8.50177e-10 0.236021 1.89246
research 0.26492 0.263193 1.53741 0.236021 4.80933e-09 1.88855
imag 1.91808 1.90158 2.33945 1.89246 1.88855 0
model 1.80501 1.77285 2.16792 1.76767 1.78488 2.7422
dataset 1.52842 1.52151 2.3276 1.53273 1.53878 2.01837
class 1.1637 1.1496 1.44823 1.1976 1.20895 2.18102
model dataset class
support 1.80501 1.52842 1.1637
granada 1.77285 1.52151 1.1496
covid 2.16792 2.3276 1.44823
programm 1.76767 1.53273 1.1976
research 1.78488 1.53878 1.20895
imag 2.7422 2.01837 2.18102
model 2.72057e-08 2.37406 2.13815
dataset 2.37406 0 1.87763
class 2.13815 1.87763 0
Pairwise-distance
support granada covid programm research imag model \
support 0 1.18984 4.44755 0.941912 0.941912 3.56952 2.99493
granada 1.18984 0 3.34914 0.54807 0.54807 2.37968 1.88382
covid 4.44755 3.34914 0 3.85527 3.85527 1.60718 2.676
programm 0.941912 0.54807 3.85527 0 0 2.76062 2.07496
research 0.941912 0.54807 3.85527 0 0 2.76062 2.07496
imag 3.56952 2.37968 1.60718 2.76062 2.76062 0 1.09614
model 2.99493 1.88382 2.676 2.07496 2.07496 1.09614 0
dataset 2.99493 1.88382 2.676 2.07496 2.07496 1.09614 0
class 0 0 0 0 0 0 0
dataset class
support 2.99493 0
granada 1.88382 0
covid 2.676 0
programm 2.07496 0
research 2.07496 0
imag 1.09614 0
model 0 0
dataset 9.31323e-09 0
class 0 0
(0.27763795369161115, 0.1765, 1.062241885835958)
mantel_test_verticle(model_science_articles,model_science_paragraph,5)
Pairwise-distance
covid imag model detect dataset class
covid 0 2.33945 2.16792 1.61978 2.3276 1.44823
imag 2.33945 0 2.7422 1.75983 2.01837 2.18102
model 2.16792 2.7422 2.72057e-08 1.68726 2.37406 2.13815
detect 1.61978 1.75983 1.68726 0 1.79884 1.49212
dataset 2.3276 2.01837 2.37406 1.79884 0 1.87763
class 1.44823 2.18102 2.13815 1.49212 1.87763 0
Pairwise-distance
covid imag model detect dataset class
covid 0 1.40496 1.40555 1.3692 1.4051 1.40077
imag 1.40496 2.10734e-08 1.40438 1.37789 1.40624 1.40121
model 1.40555 1.40438 0 1.37233 1.40535 1.40127
detect 1.3692 1.37789 1.37233 0 1.37291 1.3683
dataset 1.4051 1.40624 1.40535 1.37291 2.58096e-08 1.40167
class 1.40077 1.40121 1.40127 1.3683 1.40167 1.49012e-08
(0.6991566688423639, 0.018055555555555554, 1.783108826535045)
mantel_test_verticle(model_science_p,model_science_paragraph,5)
Pairwise-distance
covid imag model detect dataset support granada \
covid 0 1.60718 2.676 2.676 2.676 4.44755 3.34914
imag 1.60718 0 1.09614 1.09614 1.09614 3.56952 2.37968
model 2.676 1.09614 0 0 0 2.99493 1.88382
detect 2.676 1.09614 0 0 0 2.99493 1.88382
dataset 2.676 1.09614 0 0 9.31323e-09 2.99493 1.88382
support 4.44755 3.56952 2.99493 2.99493 2.99493 0 1.18984
granada 3.34914 2.37968 1.88382 1.88382 1.88382 1.18984 0
programm 3.85527 2.76062 2.07496 2.07496 2.07496 0.941912 0.54807
research 3.85527 2.76062 2.07496 2.07496 2.07496 0.941912 0.54807
programm research
covid 3.85527 3.85527
imag 2.76062 2.76062
model 2.07496 2.07496
detect 2.07496 2.07496
dataset 2.07496 2.07496
support 0.941912 0.941912
granada 0.54807 0.54807
programm 0 0
research 0 0
Pairwise-distance
covid imag model detect dataset support \
covid 0 1.40496 1.40555 1.3692 1.4051 1.03992
imag 1.40496 2.10734e-08 1.40438 1.37789 1.40624 1.04488
model 1.40555 1.40438 0 1.37233 1.40535 1.04005
detect 1.3692 1.37789 1.37233 0 1.37291 1.01011
dataset 1.4051 1.40624 1.40535 1.37291 2.58096e-08 1.04274
support 1.03992 1.04488 1.04005 1.01011 1.04274 0
granada 0.995027 0.993812 0.994144 0.948831 0.994089 0.310449
programm 0.994901 0.993977 0.994012 0.949953 0.993727 0.287824
research 1.02313 1.01966 1.02601 0.962638 1.02547 0.328554
granada programm research
covid 0.995027 0.994901 1.02313
imag 0.993812 0.993977 1.01966
model 0.994144 0.994012 1.02601
detect 0.948831 0.949953 0.962638
dataset 0.994089 0.993727 1.02547
support 0.310449 0.287824 0.328554
granada 4.65661e-10 0.032859 0.240846
programm 0.032859 0 0.233028
research 0.240846 0.233028 7.45058e-09
(0.22988121993944508, 0.1795, 1.0299334657787396)
mantel_test_verticle(model_psy_articles,model_psy_p,5)
Pairwise-distance
misinfodem covid articl health mechan psycholog \
misinfodem 1.36028e-08 1.59423 0.902727 1.69295 0.492125 1.57397
covid 1.59423 0 1.46013 2.06287 1.47337 1.71712
articl 0.902727 1.46013 0 1.5335 0.550356 1.23458
health 1.69295 2.06287 1.5335 0 1.6193 1.66727
mechan 0.492125 1.47337 0.550356 1.6193 0 1.33457
psycholog 1.57397 1.71712 1.23458 1.66727 1.33457 0
group 2.1644 2.45244 1.96966 2.61359 2.01066 2.36585
pandem 1.12183 1.32272 0.770845 1.45061 0.949488 1.29309
group pandem
misinfodem 2.1644 1.12183
covid 2.45244 1.32272
articl 1.96966 0.770845
health 2.61359 1.45061
mechan 2.01066 0.949488
psycholog 2.36585 1.29309
group 0 2.24513
pandem 2.24513 0
Pairwise-distance
misinfodem covid articl health mechan psycholog group \
misinfodem 0 1.35287 2.09875 2.98805 2.98805 2.58051 0
covid 1.35287 0 1.13552 1.67187 1.67187 1.70328 0
articl 2.09875 1.13552 0 1.22885 1.22885 0.567759 0
health 2.98805 1.67187 1.22885 0 0 1.35447 0
mechan 2.98805 1.67187 1.22885 0 0 1.35447 0
psycholog 2.58051 1.70328 0.567759 1.35447 1.35447 0 0
group 0 0 0 0 0 0 0
pandem 4.05861 2.70574 2.4577 1.22885 1.22885 2.52328 0
pandem
misinfodem 4.05861
covid 2.70574
articl 2.4577
health 1.22885
mechan 1.22885
psycholog 2.52328
group 0
pandem 2.63418e-08
(-0.6355652933311675, 0.9678, -2.007492272513091)
mantel_test_verticle(model_psy_articles,model_psy_paragraph,5)
Pairwise-distance
covid pandem health polici impact psycholog \
covid 0 1.32272 2.06287 1.47845 1.43686 1.71712
pandem 1.32272 0 1.45061 0.840065 0.875398 1.29309
health 2.06287 1.45061 0 1.6505 1.70716 1.66727
polici 1.47845 0.840065 1.6505 0 0.609937 1.20742
impact 1.43686 0.875398 1.70716 0.609937 1.36028e-08 1.38994
psycholog 1.71712 1.29309 1.66727 1.20742 1.38994 0
group 2.45244 2.24513 2.61359 2.05155 2.18441 2.36585
group
covid 2.45244
pandem 2.24513
health 2.61359
polici 2.05155
impact 2.18441
psycholog 2.36585
group 0
Pairwise-distance
covid pandem health polici impact psycholog \
covid 0 1.40424 1.39832 1.18101 1.3943 1.40124
pandem 1.40424 0 1.39659 1.17943 1.39343 1.39922
health 1.39832 1.39659 2.98023e-08 1.16945 1.38795 1.39094
polici 1.18101 1.17943 1.16945 0 1.17588 1.17297
impact 1.3943 1.39343 1.38795 1.17588 0 1.39029
psycholog 1.40124 1.39922 1.39094 1.17297 1.39029 2.58096e-08
group 1.39464 1.39078 1.38352 1.16873 1.3845 1.38857
group
covid 1.39464
pandem 1.39078
health 1.38352
polici 1.16873
impact 1.3845
psycholog 1.38857
group 0
(0.37749281960674924, 0.173015873015873, 0.9466163475412356)
mantel_test_verticle(model_psy_p,model_psy_paragraph,5)
Pairwise-distance
covid pandem health polici impact misinfodem articl \
covid 0 2.70574 1.67187 0 2.236 1.35287 1.13552
pandem 2.70574 2.63418e-08 1.22885 0 1.89336 4.05861 2.4577
health 1.67187 1.22885 0 0 1.0317 2.98805 1.22885
polici 0 0 0 0 0 0 0
impact 2.236 1.89336 1.0317 0 0 3.34632 1.25066
misinfodem 1.35287 4.05861 2.98805 0 3.34632 0 2.09875
articl 1.13552 2.4577 1.22885 0 1.25066 2.09875 0
mechan 1.67187 1.22885 0 0 1.0317 2.98805 1.22885
mechan
covid 1.67187
pandem 1.22885
health 0
polici 0
impact 1.0317
misinfodem 2.98805
articl 1.22885
mechan 0
Pairwise-distance
covid pandem health polici impact misinfodem \
covid 0 1.40424 1.39832 1.18101 1.3943 1.01939
pandem 1.40424 0 1.39659 1.17943 1.39343 1.01529
health 1.39832 1.39659 2.98023e-08 1.16945 1.38795 1.02419
polici 1.18101 1.17943 1.16945 0 1.17588 0.688077
impact 1.3943 1.39343 1.38795 1.17588 0 1.01745
misinfodem 1.01939 1.01529 1.02419 0.688077 1.01745 7.45058e-09
articl 1.3135 1.30856 1.30736 1.07248 1.30274 0.87454
mechan 0.99498 0.993759 0.985503 0.638435 0.985301 0.216625
articl mechan
covid 1.3135 0.99498
pandem 1.30856 0.993759
health 1.30736 0.985503
polici 1.07248 0.638435
impact 1.30274 0.985301
misinfodem 0.87454 0.216625
articl 0 0.862229
mechan 0.862229 0
(-0.021563880670777167, 0.4867, -0.06129678024705728)
mantel_test_verticle(model_science_articles,model_science_p,10)
Pairwise-distance
support granada covid research programm tabik \
support 0 0.168942 1.53014 0.26492 0.161791 0.200379
granada 0.168942 0 1.59104 0.263193 0.0865721 0.0649291
covid 1.53014 1.59104 0 1.53741 1.60946 1.5802
research 0.26492 0.263193 1.53741 4.80933e-09 0.236021 0.298799
programm 0.161791 0.0865721 1.60946 0.236021 8.50177e-10 0.151501
tabik 0.200379 0.0649291 1.5802 0.298799 0.151501 3.40071e-09
garcía 0.158953 0.0507223 1.59651 0.244374 0.0407668 0.114423
decsai 0.159255 0.0649291 1.60444 0.240191 0.021643 0.129858
project 0.279033 0.282792 1.62273 0.307197 0.229627 0.332025
gómez 0.159282 0.0717116 1.60134 0.238796 0.0220085 0.135916
imag 1.91808 1.90158 2.33945 1.88855 1.89246 1.91096
model 1.80501 1.77285 2.16792 1.78488 1.76767 1.77949
dataset 1.52842 1.52151 2.3276 1.53878 1.53273 1.51628
class 1.1637 1.1496 1.44823 1.20895 1.1976 1.11666
perform 0.924888 0.969531 1.68452 0.962291 0.934697 0.999789
learn 1.35236 1.36047 1.89994 1.34907 1.34725 1.37388
detect 0.912255 0.896688 1.61978 0.834118 0.857448 0.930328
use 0.46925 0.601258 1.37432 0.629794 0.583067 0.6225
base 0.557941 0.547801 1.30498 0.563681 0.540503 0.562031
garcía decsai project gómez imag model \
support 0.158953 0.159255 0.279033 0.159282 1.91808 1.80501
granada 0.0507223 0.0649291 0.282792 0.0717116 1.90158 1.77285
covid 1.59651 1.60444 1.62273 1.60134 2.33945 2.16792
research 0.244374 0.240191 0.307197 0.238796 1.88855 1.78488
programm 0.0407668 0.021643 0.229627 0.0220085 1.89246 1.76767
tabik 0.114423 0.129858 0.332025 0.135916 1.91096 1.77949
garcía 0 0.0220085 0.24045 0.021643 1.89376 1.77194
decsai 0.0220085 0 0.24111 0.0156096 1.89438 1.76857
project 0.24045 0.24111 0 0.22738 1.84183 1.82021
gómez 0.021643 0.0156096 0.22738 0 1.89166 1.77085
imag 1.89376 1.89438 1.84183 1.89166 0 2.7422
model 1.77194 1.76857 1.82021 1.77085 2.7422 2.72057e-08
dataset 1.53087 1.52947 1.5324 1.5339 2.01837 2.37406
class 1.16594 1.18519 1.15644 1.17825 2.18102 2.13815
perform 0.950934 0.942781 0.946773 0.942544 2.09899 1.42377
learn 1.35218 1.35005 1.34388 1.34912 2.33698 2.1051
detect 0.872296 0.866614 0.832302 0.86278 1.75983 1.68726
use 0.591115 0.58647 0.622878 0.587135 1.89132 1.88147
base 0.53881 0.541039 0.569539 0.537612 1.86736 1.7136
dataset class perform learn detect use \
support 1.52842 1.1637 0.924888 1.35236 0.912255 0.46925
granada 1.52151 1.1496 0.969531 1.36047 0.896688 0.601258
covid 2.3276 1.44823 1.68452 1.89994 1.61978 1.37432
research 1.53878 1.20895 0.962291 1.34907 0.834118 0.629794
programm 1.53273 1.1976 0.934697 1.34725 0.857448 0.583067
tabik 1.51628 1.11666 0.999789 1.37388 0.930328 0.6225
garcía 1.53087 1.16594 0.950934 1.35218 0.872296 0.591115
decsai 1.52947 1.18519 0.942781 1.35005 0.866614 0.58647
project 1.5324 1.15644 0.946773 1.34388 0.832302 0.622878
gómez 1.5339 1.17825 0.942544 1.34912 0.86278 0.587135
imag 2.01837 2.18102 2.09899 2.33698 1.75983 1.89132
model 2.37406 2.13815 1.42377 2.1051 1.68726 1.88147
dataset 0 1.87763 1.7877 2.01687 1.79884 1.58888
class 1.87763 0 1.61368 1.785 1.49212 1.34592
perform 1.7877 1.61368 1.92373e-08 1.49132 1.10838 0.963038
learn 2.01687 1.785 1.49132 0 1.6455 1.36097
detect 1.79884 1.49212 1.10838 1.6455 0 0.984045
use 1.58888 1.34592 0.963038 1.36097 0.984045 0
base 1.67631 1.2082 1.00457 1.40083 0.705965 0.644773
base
support 0.557941
granada 0.547801
covid 1.30498
research 0.563681
programm 0.540503
tabik 0.562031
garcía 0.53881
decsai 0.541039
project 0.569539
gómez 0.537612
imag 1.86736
model 1.7136
dataset 1.67631
class 1.2082
perform 1.00457
learn 1.40083
detect 0.705965
use 0.644773
base 9.61866e-09
Pairwise-distance
support granada covid research programm tabik garcía \
support 0 1.18984 4.44755 0.941912 0.941912 1.62443 2.37968
granada 1.18984 0 3.34914 0.54807 0.54807 0.470956 1.18984
covid 4.44755 3.34914 0 3.85527 3.85527 3.08827 2.34183
research 0.941912 0.54807 3.85527 0 0 0.797652 1.5953
programm 0.941912 0.54807 3.85527 0 0 0.797652 1.5953
tabik 1.62443 0.470956 3.08827 0.797652 0.797652 0 0.797652
garcía 2.37968 1.18984 2.34183 1.5953 1.5953 0.797652 0
decsai 1.41287 0.638836 3.61521 0.470956 0.470956 0.54807 1.28509
project 1.41287 0.638836 3.61521 0.470956 0.470956 0.54807 1.28509
gómez 2.07496 0.941912 2.88126 1.18984 1.18984 0.470956 0.54807
imag 3.56952 2.37968 1.60718 2.76062 2.76062 1.97031 1.18984
model 2.99493 1.88382 2.676 2.07496 2.07496 1.41287 0.979511
dataset 2.99493 1.88382 2.676 2.07496 2.07496 1.41287 0.979511
class 0 0 0 0 0 0 0
perform 0 0 0 0 0 0 0
learn 2.99493 1.88382 2.676 2.07496 2.07496 1.41287 0.979511
detect 2.99493 1.88382 2.676 2.07496 2.07496 1.41287 0.979511
use 2.99493 1.88382 2.676 2.07496 2.07496 1.41287 0.979511
base 2.99493 1.88382 2.676 2.07496 2.07496 1.41287 0.979511
decsai project gómez imag model dataset class \
support 1.41287 1.41287 2.07496 3.56952 2.99493 2.99493 0
granada 0.638836 0.638836 0.941912 2.37968 1.88382 1.88382 0
covid 3.61521 3.61521 2.88126 1.60718 2.676 2.676 0
research 0.470956 0.470956 1.18984 2.76062 2.07496 2.07496 0
programm 0.470956 0.470956 1.18984 2.76062 2.07496 2.07496 0
tabik 0.54807 0.54807 0.470956 1.97031 1.41287 1.41287 0
garcía 1.28509 1.28509 0.54807 1.18984 0.979511 0.979511 0
decsai 0 0 0.797652 2.39296 1.62443 1.62443 0
project 0 0 0.797652 2.39296 1.62443 1.62443 0
gómez 0.797652 0.797652 0 1.5953 0.941912 0.941912 0
imag 2.39296 2.39296 1.5953 0 1.09614 1.09614 0
model 1.62443 1.62443 0.941912 1.09614 0 0 0
dataset 1.62443 1.62443 0.941912 1.09614 0 9.31323e-09 0
class 0 0 0 0 0 0 0
perform 0 0 0 0 0 0 0
learn 1.62443 1.62443 0.941912 1.09614 0 6.58545e-09 0
detect 1.62443 1.62443 0.941912 1.09614 0 0 0
use 1.62443 1.62443 0.941912 1.09614 0 0 0
base 1.62443 1.62443 0.941912 1.09614 0 6.58545e-09 0
perform learn detect use base
support 0 2.99493 2.99493 2.99493 2.99493
granada 0 1.88382 1.88382 1.88382 1.88382
covid 0 2.676 2.676 2.676 2.676
research 0 2.07496 2.07496 2.07496 2.07496
programm 0 2.07496 2.07496 2.07496 2.07496
tabik 0 1.41287 1.41287 1.41287 1.41287
garcía 0 0.979511 0.979511 0.979511 0.979511
decsai 0 1.62443 1.62443 1.62443 1.62443
project 0 1.62443 1.62443 1.62443 1.62443
gómez 0 0.941912 0.941912 0.941912 0.941912
imag 0 1.09614 1.09614 1.09614 1.09614
model 0 0 0 0 0
dataset 0 6.58545e-09 0 0 6.58545e-09
class 0 0 0 0 0
perform 0 0 0 0 0
learn 0 9.31323e-09 6.58545e-09 6.58545e-09 9.31323e-09
detect 0 6.58545e-09 0 0 6.58545e-09
use 0 6.58545e-09 0 0 6.58545e-09
base 0 9.31323e-09 6.58545e-09 6.58545e-09 9.31323e-09
(0.13258127075526377, 0.2438, 0.7244119003984184)
mantel_test_verticle(model_science_articles,model_science_paragraph,10)
Pairwise-distance
covid imag model detect dataset learn use \
covid 0 2.33945 2.16792 1.61978 2.3276 1.89994 1.37432
imag 2.33945 0 2.7422 1.75983 2.01837 2.33698 1.89132
model 2.16792 2.7422 2.72057e-08 1.68726 2.37406 2.1051 1.88147
detect 1.61978 1.75983 1.68726 0 1.79884 1.6455 0.984045
dataset 2.3276 2.01837 2.37406 1.79884 0 2.01687 1.58888
learn 1.89994 2.33698 2.1051 1.6455 2.01687 0 1.36097
use 1.37432 1.89132 1.88147 0.984045 1.58888 1.36097 0
class 1.44823 2.18102 2.13815 1.49212 1.87763 1.785 1.34592
base 1.30498 1.86736 1.7136 0.705965 1.67631 1.40083 0.644773
chest 1.58257 1.71112 1.88322 0.887614 1.79825 1.51976 0.806383
perform 1.68452 2.09899 1.42377 1.10838 1.7877 1.49132 0.963038
class base chest perform
covid 1.44823 1.30498 1.58257 1.68452
imag 2.18102 1.86736 1.71112 2.09899
model 2.13815 1.7136 1.88322 1.42377
detect 1.49212 0.705965 0.887614 1.10838
dataset 1.87763 1.67631 1.79825 1.7877
learn 1.785 1.40083 1.51976 1.49132
use 1.34592 0.644773 0.806383 0.963038
class 0 1.2082 1.27703 1.61368
base 1.2082 9.61866e-09 0.783388 1.00457
chest 1.27703 0.783388 0 1.08675
perform 1.61368 1.00457 1.08675 1.92373e-08
Pairwise-distance
covid imag model detect dataset learn \
covid 0 1.40496 1.40555 1.3692 1.4051 1.39767
imag 1.40496 2.10734e-08 1.40438 1.37789 1.40624 1.39621
model 1.40555 1.40438 0 1.37233 1.40535 1.39539
detect 1.3692 1.37789 1.37233 0 1.37291 1.36974
dataset 1.4051 1.40624 1.40535 1.37291 2.58096e-08 1.39554
learn 1.39767 1.39621 1.39539 1.36974 1.39554 2.10734e-08
use 1.40336 1.40323 1.40291 1.37004 1.40296 1.39513
class 1.40077 1.40121 1.40127 1.3683 1.40167 1.39051
base 1.39171 1.39219 1.39106 1.36252 1.39153 1.37923
chest 1.35387 1.34247 1.34782 1.31881 1.3413 1.32913
perform 1.39974 1.39938 1.40094 1.37039 1.39905 1.39225
use class base chest perform
covid 1.40336 1.40077 1.39171 1.35387 1.39974
imag 1.40323 1.40121 1.39219 1.34247 1.39938
model 1.40291 1.40127 1.39106 1.34782 1.40094
detect 1.37004 1.3683 1.36252 1.31881 1.37039
dataset 1.40296 1.40167 1.39153 1.3413 1.39905
learn 1.39513 1.39051 1.37923 1.32913 1.39225
use 1.49012e-08 1.40024 1.3913 1.34 1.40017
class 1.40024 1.49012e-08 1.39123 1.33915 1.39581
base 1.3913 1.39123 0 1.32714 1.38573
chest 1.34 1.33915 1.32714 0 1.33941
perform 1.40017 1.39581 1.38573 1.33941 2.10734e-08
(0.49915675662715714, 0.0257, 1.6653415184984917)
mantel_test_verticle(model_science_p,model_science_paragraph,10)
Pairwise-distance
covid imag model detect dataset learn \
covid 0 1.60718 2.676 2.676 2.676 2.676
imag 1.60718 0 1.09614 1.09614 1.09614 1.09614
model 2.676 1.09614 0 0 0 0
detect 2.676 1.09614 0 0 0 6.58545e-09
dataset 2.676 1.09614 0 0 9.31323e-09 6.58545e-09
learn 2.676 1.09614 0 6.58545e-09 6.58545e-09 9.31323e-09
use 2.676 1.09614 0 0 0 6.58545e-09
class 0 0 0 0 0 0
base 2.676 1.09614 0 6.58545e-09 6.58545e-09 9.31323e-09
chest 2.13813 0.54807 0.54807 0.54807 0.54807 0.54807
support 4.44755 3.56952 2.99493 2.99493 2.99493 2.99493
granada 3.34914 2.37968 1.88382 1.88382 1.88382 1.88382
research 3.85527 2.76062 2.07496 2.07496 2.07496 2.07496
programm 3.85527 2.76062 2.07496 2.07496 2.07496 2.07496
tabik 3.08827 1.97031 1.41287 1.41287 1.41287 1.41287
garcía 2.34183 1.18984 0.979511 0.979511 0.979511 0.979511
decsai 3.61521 2.39296 1.62443 1.62443 1.62443 1.62443
project 3.61521 2.39296 1.62443 1.62443 1.62443 1.62443
gómez 2.88126 1.5953 0.941912 0.941912 0.941912 0.941912
use class base chest support granada \
covid 2.676 0 2.676 2.13813 4.44755 3.34914
imag 1.09614 0 1.09614 0.54807 3.56952 2.37968
model 0 0 0 0.54807 2.99493 1.88382
detect 0 0 6.58545e-09 0.54807 2.99493 1.88382
dataset 0 0 6.58545e-09 0.54807 2.99493 1.88382
learn 6.58545e-09 0 9.31323e-09 0.54807 2.99493 1.88382
use 0 0 6.58545e-09 0.54807 2.99493 1.88382
class 0 0 0 0 0 0
base 6.58545e-09 0 9.31323e-09 0.54807 2.99493 1.88382
chest 0.54807 0 0.54807 0 3.24887 2.07496
support 2.99493 0 2.99493 3.24887 0 1.18984
granada 1.88382 0 1.88382 2.07496 1.18984 0
research 2.07496 0 2.07496 2.37968 0.941912 0.54807
programm 2.07496 0 2.07496 2.37968 0.941912 0.54807
tabik 1.41287 0 1.41287 1.62443 1.62443 0.470956
garcía 0.979511 0 0.979511 0.941912 2.37968 1.18984
decsai 1.62443 0 1.62443 1.97031 1.41287 0.638836
project 1.62443 0 1.62443 1.97031 1.41287 0.638836
gómez 0.941912 0 0.941912 1.18984 2.07496 0.941912
research programm tabik garcía decsai project gómez
covid 3.85527 3.85527 3.08827 2.34183 3.61521 3.61521 2.88126
imag 2.76062 2.76062 1.97031 1.18984 2.39296 2.39296 1.5953
model 2.07496 2.07496 1.41287 0.979511 1.62443 1.62443 0.941912
detect 2.07496 2.07496 1.41287 0.979511 1.62443 1.62443 0.941912
dataset 2.07496 2.07496 1.41287 0.979511 1.62443 1.62443 0.941912
learn 2.07496 2.07496 1.41287 0.979511 1.62443 1.62443 0.941912
use 2.07496 2.07496 1.41287 0.979511 1.62443 1.62443 0.941912
class 0 0 0 0 0 0 0
base 2.07496 2.07496 1.41287 0.979511 1.62443 1.62443 0.941912
chest 2.37968 2.37968 1.62443 0.941912 1.97031 1.97031 1.18984
support 0.941912 0.941912 1.62443 2.37968 1.41287 1.41287 2.07496
granada 0.54807 0.54807 0.470956 1.18984 0.638836 0.638836 0.941912
research 0 0 0.797652 1.5953 0.470956 0.470956 1.18984
programm 0 0 0.797652 1.5953 0.470956 0.470956 1.18984
tabik 0.797652 0.797652 0 0.797652 0.54807 0.54807 0.470956
garcía 1.5953 1.5953 0.797652 0 1.28509 1.28509 0.54807
decsai 0.470956 0.470956 0.54807 1.28509 0 0 0.797652
project 0.470956 0.470956 0.54807 1.28509 0 0 0.797652
gómez 1.18984 1.18984 0.470956 0.54807 0.797652 0.797652 0
Pairwise-distance
covid imag model detect dataset learn \
covid 0 1.40496 1.40555 1.3692 1.4051 1.39767
imag 1.40496 2.10734e-08 1.40438 1.37789 1.40624 1.39621
model 1.40555 1.40438 0 1.37233 1.40535 1.39539
detect 1.3692 1.37789 1.37233 0 1.37291 1.36974
dataset 1.4051 1.40624 1.40535 1.37291 2.58096e-08 1.39554
learn 1.39767 1.39621 1.39539 1.36974 1.39554 2.10734e-08
use 1.40336 1.40323 1.40291 1.37004 1.40296 1.39513
class 1.40077 1.40121 1.40127 1.3683 1.40167 1.39051
base 1.39171 1.39219 1.39106 1.36252 1.39153 1.37923
chest 1.35387 1.34247 1.34782 1.31881 1.3413 1.32913
support 1.03992 1.04488 1.04005 1.01011 1.04274 1.04058
granada 0.995027 0.993812 0.994144 0.948831 0.994089 0.981639
research 1.02313 1.01966 1.02601 0.962638 1.02547 1.01492
programm 0.994901 0.993977 0.994012 0.949953 0.993727 0.982201
tabik 0.987813 0.991898 0.998183 0.959294 0.985325 0.98497
garcía 0.994578 0.99333 0.993713 0.948831 0.993357 0.981242
decsai 0.994572 0.993338 0.993544 0.94882 0.993303 0.981023
project 1.00202 0.999023 1.00121 0.953546 0.998148 0.991151
gómez 0.994565 0.993426 0.993702 0.949039 0.993318 0.981266
use class base chest support granada \
covid 1.40336 1.40077 1.39171 1.35387 1.03992 0.995027
imag 1.40323 1.40121 1.39219 1.34247 1.04488 0.993812
model 1.40291 1.40127 1.39106 1.34782 1.04005 0.994144
detect 1.37004 1.3683 1.36252 1.31881 1.01011 0.948831
dataset 1.40296 1.40167 1.39153 1.3413 1.04274 0.994089
learn 1.39513 1.39051 1.37923 1.32913 1.04058 0.981639
use 1.49012e-08 1.40024 1.3913 1.34 1.04288 0.990765
class 1.40024 1.49012e-08 1.39123 1.33915 1.03416 0.988247
base 1.3913 1.39123 0 1.32714 1.02338 0.97343
chest 1.34 1.33915 1.32714 0 0.94813 0.90672
support 1.04288 1.03416 1.02338 0.94813 0 0.310449
granada 0.990765 0.988247 0.97343 0.90672 0.310449 4.65661e-10
research 1.02154 1.01538 0.995403 0.935802 0.328554 0.240846
programm 0.991484 0.98795 0.973484 0.907467 0.287824 0.032859
tabik 0.996508 0.991715 0.954761 0.878189 0.312154 0.0595399
garcía 0.990912 0.987709 0.973212 0.907908 0.310243 0.0226287
decsai 0.99085 0.987747 0.973076 0.907984 0.309628 0.0222633
project 0.999875 0.990083 0.979392 0.917089 0.299808 0.113611
gómez 0.990972 0.987726 0.973207 0.907813 0.306612 0.022988
research programm tabik garcía decsai project \
covid 1.02313 0.994901 0.987813 0.994578 0.994572 1.00202
imag 1.01966 0.993977 0.991898 0.99333 0.993338 0.999023
model 1.02601 0.994012 0.998183 0.993713 0.993544 1.00121
detect 0.962638 0.949953 0.959294 0.948831 0.94882 0.953546
dataset 1.02547 0.993727 0.985325 0.993357 0.993303 0.998148
learn 1.01492 0.982201 0.98497 0.981242 0.981023 0.991151
use 1.02154 0.991484 0.996508 0.990912 0.99085 0.999875
class 1.01538 0.98795 0.991715 0.987709 0.987747 0.990083
base 0.995403 0.973484 0.954761 0.973212 0.973076 0.979392
chest 0.935802 0.907467 0.878189 0.907908 0.907984 0.917089
support 0.328554 0.287824 0.312154 0.310243 0.309628 0.299808
granada 0.240846 0.032859 0.0595399 0.0226287 0.0222633 0.113611
research 7.45058e-09 0.233028 0.252747 0.243118 0.243634 0.248982
programm 0.233028 0 0.0607255 0.0244146 0.0233586 0.107361
tabik 0.252747 0.0607255 0 0.0561834 0.056703 0.121669
garcía 0.243118 0.0244146 0.0561834 0 0.0061094 0.109594
decsai 0.243634 0.0233586 0.056703 0.0061094 0 0.109892
project 0.248982 0.107361 0.121669 0.109594 0.109892 0
gómez 0.242569 0.0206644 0.0560565 0.00439174 0.00571723 0.10894
gómez
covid 0.994565
imag 0.993426
model 0.993702
detect 0.949039
dataset 0.993318
learn 0.981266
use 0.990972
class 0.987726
base 0.973207
chest 0.907813
support 0.306612
granada 0.022988
research 0.242569
programm 0.0206644
tabik 0.0560565
garcía 0.00439174
decsai 0.00571723
project 0.10894
gómez 0
(0.029764461120292685, 0.4411, 0.18913563513179427)
mantel_test_verticle(model_psy_articles,model_psy_p,10)
Pairwise-distance
misinfodem covid articl health mechan commun \
misinfodem 1.36028e-08 1.59423 0.902727 1.69295 0.492125 0.557808
covid 1.59423 0 1.46013 2.06287 1.47337 1.50049
articl 0.902727 1.46013 0 1.5335 0.550356 0.798937
health 1.69295 2.06287 1.5335 0 1.6193 1.62403
mechan 0.492125 1.47337 0.550356 1.6193 0 0.433102
commun 0.557808 1.50049 0.798937 1.62403 0.433102 0
psycholog 1.57397 1.71712 1.23458 1.66727 1.33457 1.25524
strategi 0.408792 1.49023 0.606565 1.59347 0.1669 0.383342
misinform 0.258613 1.51955 0.694399 1.63517 0.234499 0.425667
inform 0.476618 1.49355 0.735753 1.72274 0.389701 0.349695
group 2.1644 2.45244 1.96966 2.61359 2.01066 2.16182
pandem 1.12183 1.32272 0.770845 1.45061 0.949488 1.10867
peopl 1.42213 1.86567 1.23285 2.18477 1.28403 1.29756
impact 1.06517 1.43686 0.541267 1.70716 0.872451 1.07137
social 0.960577 1.6669 1.1423 1.78312 0.944618 0.952591
studi 1.05214 1.10668 0.992271 1.85649 0.806163 0.926028
research 1.57553 1.85642 1.46791 2.14506 1.40045 1.216
psycholog strategi misinform inform group pandem \
misinfodem 1.57397 0.408792 0.258613 0.476618 2.1644 1.12183
covid 1.71712 1.49023 1.51955 1.49355 2.45244 1.32272
articl 1.23458 0.606565 0.694399 0.735753 1.96966 0.770845
health 1.66727 1.59347 1.63517 1.72274 2.61359 1.45061
mechan 1.33457 0.1669 0.234499 0.389701 2.01066 0.949488
commun 1.25524 0.383342 0.425667 0.349695 2.16182 1.10867
psycholog 0 1.34872 1.42905 1.36647 2.36585 1.29309
strategi 1.34872 0 0.180432 0.378132 2.04866 0.975622
misinform 1.42905 0.180432 0 0.352715 2.0764 1.00827
inform 1.36647 0.378132 0.352715 0 2.0741 1.0411
group 2.36585 2.04866 2.0764 2.0741 0 2.24513
pandem 1.29309 0.975622 1.00827 1.0411 2.24513 0
peopl 1.77965 1.32853 1.32846 1.13912 2.00935 1.51763
impact 1.38994 0.878036 0.933853 1.02881 2.18441 0.875398
social 1.56908 0.953546 0.920866 0.761349 1.89647 1.34763
studi 1.36025 0.83285 0.903747 0.994846 2.0032 1.23384
research 1.19442 1.37128 1.46212 1.41718 2.39065 1.7469
peopl impact social studi research
misinfodem 1.42213 1.06517 0.960577 1.05214 1.57553
covid 1.86567 1.43686 1.6669 1.10668 1.85642
articl 1.23285 0.541267 1.1423 0.992271 1.46791
health 2.18477 1.70716 1.78312 1.85649 2.14506
mechan 1.28403 0.872451 0.944618 0.806163 1.40045
commun 1.29756 1.07137 0.952591 0.926028 1.216
psycholog 1.77965 1.38994 1.56908 1.36025 1.19442
strategi 1.32853 0.878036 0.953546 0.83285 1.37128
misinform 1.32846 0.933853 0.920866 0.903747 1.46212
inform 1.13912 1.02881 0.761349 0.994846 1.41718
group 2.00935 2.18441 1.89647 2.0032 2.39065
pandem 1.51763 0.875398 1.34763 1.23384 1.7469
peopl 0 1.48974 1.23727 1.68566 1.97039
impact 1.48974 1.36028e-08 1.41787 1.0847 1.54162
social 1.23727 1.41787 0 1.2903 1.68404
studi 1.68566 1.0847 1.2903 0 1.26559
research 1.97039 1.54162 1.68404 1.26559 0
Pairwise-distance
misinfodem covid articl health mechan commun \
misinfodem 0 1.35287 2.09875 2.98805 2.98805 2.98805
covid 1.35287 0 1.13552 1.67187 1.67187 1.67187
articl 2.09875 1.13552 0 1.22885 1.22885 1.22885
health 2.98805 1.67187 1.22885 0 0 0
mechan 2.98805 1.67187 1.22885 0 0 0
commun 2.98805 1.67187 1.22885 0 0 0
psycholog 2.58051 1.70328 0.567759 1.35447 1.35447 1.35447
strategi 2.58051 1.70328 0.567759 1.35447 1.35447 1.35447
misinform 3.60765 2.25478 2.0571 0.841095 0.841095 0.841095
inform 2.91809 1.72034 0.901913 0.558152 0.558152 0.558152
group 0 0 0 0 0 0
pandem 4.05861 2.70574 2.4577 1.22885 1.22885 1.22885
peopl 2.91809 1.72034 0.901913 0.558152 0.558152 0.558152
impact 3.34632 2.236 1.25066 1.0317 1.0317 1.0317
social 4.05861 2.70574 2.4577 1.22885 1.22885 1.22885
studi 0 0 0 0 0 0
research 3.77527 2.50132 1.80383 0.860169 0.860169 0.860169
psycholog strategi misinform inform group pandem \
misinfodem 2.58051 2.58051 3.60765 2.91809 0 4.05861
covid 1.70328 1.70328 2.25478 1.72034 0 2.70574
articl 0.567759 0.567759 2.0571 0.901913 0 2.4577
health 1.35447 1.35447 0.841095 0.558152 0 1.22885
mechan 1.35447 1.35447 0.841095 0.558152 0 1.22885
commun 1.35447 1.35447 0.841095 0.558152 0 1.22885
psycholog 0 0 2.18461 0.841095 0 2.52328
strategi 0 0 2.18461 0.841095 0 2.52328
misinform 2.18461 2.18461 0 1.35447 0 0.450957
inform 0.841095 0.841095 1.35447 0 0 1.68219
group 0 0 0 0 0 0
pandem 2.52328 2.52328 0.450957 1.68219 0 2.63418e-08
peopl 0.841095 0.841095 1.35447 9.31323e-09 0 1.68219
impact 0.901913 0.901913 1.67445 0.567759 0 1.89336
social 2.52328 2.52328 0.450957 1.68219 0 2.63418e-08
studi 0 0 0 0 0 0
research 1.64907 1.64907 1.0317 0.901913 0 1.1163
peopl impact social studi research
misinfodem 2.91809 3.34632 4.05861 0 3.77527
covid 1.72034 2.236 2.70574 0 2.50132
articl 0.901913 1.25066 2.4577 0 1.80383
health 0.558152 1.0317 1.22885 0 0.860169
mechan 0.558152 1.0317 1.22885 0 0.860169
commun 0.558152 1.0317 1.22885 0 0.860169
psycholog 0.841095 0.901913 2.52328 0 1.64907
strategi 0.841095 0.901913 2.52328 0 1.64907
misinform 1.35447 1.67445 0.450957 0 1.0317
inform 9.31323e-09 0.567759 1.68219 0 0.901913
group 0 0 0 0 0
pandem 1.68219 1.89336 2.63418e-08 0 1.1163
peopl 0 0.567759 1.68219 0 0.901913
impact 0.567759 0 1.89336 0 0.841095
social 1.68219 1.89336 2.63418e-08 0 1.1163
studi 0 0 0 0 0
research 0.901913 0.841095 1.1163 0 9.31323e-09
(-0.2884071564199686, 0.9284, -1.4702499292889604)
mantel_test_verticle(model_psy_articles,model_psy_paragraph,10)
Pairwise-distance
covid pandem health polici impact countri \
covid 0 1.32272 2.06287 1.47845 1.43686 1.19061
pandem 1.32272 0 1.45061 0.840065 0.875398 1.37045
health 2.06287 1.45061 0 1.6505 1.70716 2.08939
polici 1.47845 0.840065 1.6505 0 0.609937 1.16456
impact 1.43686 0.875398 1.70716 0.609937 1.36028e-08 1.39973
countri 1.19061 1.37045 2.08939 1.16456 1.39973 1.92373e-08
develop 1.5276 0.774868 1.61764 0.382845 0.510095 1.26566
nation 1.52618 0.901196 1.5652 0.410922 0.73547 1.21104
sustain 1.5939 0.879967 1.78022 0.455998 0.405692 1.31942
economi 1.48395 0.82434 1.74574 0.369323 0.417327 1.2103
psycholog 1.71712 1.29309 1.66727 1.20742 1.38994 1.56731
group 2.45244 2.24513 2.61359 2.05155 2.18441 2.41425
peopl 1.86567 1.51763 2.18477 1.24316 1.48974 1.76839
social 1.6669 1.34763 1.78312 1.10936 1.41787 1.49008
studi 1.10668 1.23384 1.85649 0.9031 1.0847 0.885375
research 1.85642 1.7469 2.14506 1.38514 1.54162 1.40456
develop nation sustain economi psycholog group \
covid 1.5276 1.52618 1.5939 1.48395 1.71712 2.45244
pandem 0.774868 0.901196 0.879967 0.82434 1.29309 2.24513
health 1.61764 1.5652 1.78022 1.74574 1.66727 2.61359
polici 0.382845 0.410922 0.455998 0.369323 1.20742 2.05155
impact 0.510095 0.73547 0.405692 0.417327 1.38994 2.18441
countri 1.26566 1.21104 1.31942 1.2103 1.56731 2.41425
develop 0 0.612948 0.332283 0.308344 1.23001 2.10616
nation 0.612948 6.80142e-09 0.665881 0.572731 1.36464 2.0783
sustain 0.332283 0.665881 1.36028e-08 0.17173 1.3954 2.14035
economi 0.308344 0.572731 0.17173 9.61866e-09 1.38274 2.13791
psycholog 1.23001 1.36464 1.3954 1.38274 0 2.36585
group 2.10616 2.0783 2.14035 2.13791 2.36585 0
peopl 1.33626 1.27791 1.34347 1.30761 1.77965 2.00935
social 1.19599 1.09806 1.34228 1.29889 1.56908 1.89647
studi 1.06748 0.943296 1.15987 1.04334 1.36025 2.0032
research 1.49114 1.48786 1.58265 1.55864 1.19442 2.39065
peopl social studi research
covid 1.86567 1.6669 1.10668 1.85642
pandem 1.51763 1.34763 1.23384 1.7469
health 2.18477 1.78312 1.85649 2.14506
polici 1.24316 1.10936 0.9031 1.38514
impact 1.48974 1.41787 1.0847 1.54162
countri 1.76839 1.49008 0.885375 1.40456
develop 1.33626 1.19599 1.06748 1.49114
nation 1.27791 1.09806 0.943296 1.48786
sustain 1.34347 1.34228 1.15987 1.58265
economi 1.30761 1.29889 1.04334 1.55864
psycholog 1.77965 1.56908 1.36025 1.19442
group 2.00935 1.89647 2.0032 2.39065
peopl 0 1.23727 1.68566 1.97039
social 1.23727 0 1.2903 1.68404
studi 1.68566 1.2903 0 1.26559
research 1.97039 1.68404 1.26559 0
Pairwise-distance
covid pandem health polici impact countri \
covid 0 1.40424 1.39832 1.18101 1.3943 1.39452
pandem 1.40424 0 1.39659 1.17943 1.39343 1.39162
health 1.39832 1.39659 2.98023e-08 1.16945 1.38795 1.38601
polici 1.18101 1.17943 1.16945 0 1.17588 1.17229
impact 1.3943 1.39343 1.38795 1.17588 0 1.38641
countri 1.39452 1.39162 1.38601 1.17229 1.38641 2.98023e-08
develop 1.38326 1.38447 1.37717 1.15938 1.37587 1.36229
nation 1.22899 1.22584 1.22499 0.837969 1.22654 1.23319
sustain 1.35901 1.3518 1.34372 1.13024 1.34354 1.35344
economi 1.36338 1.36026 1.35577 1.13045 1.3521 1.35641
psycholog 1.40124 1.39922 1.39094 1.17297 1.39029 1.38934
group 1.39464 1.39078 1.38352 1.16873 1.3845 1.38134
peopl 1.40211 1.39901 1.39488 1.18087 1.39323 1.38996
social 1.37457 1.36797 1.36012 1.1593 1.35911 1.35786
studi 1.3996 1.39568 1.38864 1.16752 1.3894 1.38996
research 1.38695 1.38805 1.37808 1.15817 1.37858 1.37654
develop nation sustain economi psycholog group \
covid 1.38326 1.22899 1.35901 1.36338 1.40124 1.39464
pandem 1.38447 1.22584 1.3518 1.36026 1.39922 1.39078
health 1.37717 1.22499 1.34372 1.35577 1.39094 1.38352
polici 1.15938 0.837969 1.13024 1.13045 1.17297 1.16873
impact 1.37587 1.22654 1.34354 1.3521 1.39029 1.3845
countri 1.36229 1.23319 1.35344 1.35641 1.38934 1.38134
develop 0 1.15853 1.30987 1.32839 1.37953 1.3717
nation 1.15853 2.10734e-08 1.18406 1.23581 1.22554 1.214
sustain 1.30987 1.18406 0 1.32734 1.33487 1.34745
economi 1.32839 1.23581 1.32734 0 1.34889 1.34992
psycholog 1.37953 1.22554 1.33487 1.34889 2.58096e-08 1.38857
group 1.3717 1.214 1.34745 1.34992 1.38857 0
peopl 1.37452 1.22695 1.35513 1.36081 1.39601 1.38894
social 1.34745 1.18433 1.31744 1.31772 1.37095 1.35866
studi 1.37523 1.21671 1.35226 1.35133 1.39514 1.38631
research 1.36426 1.19852 1.3439 1.33826 1.38225 1.37627
peopl social studi research
covid 1.40211 1.37457 1.3996 1.38695
pandem 1.39901 1.36797 1.39568 1.38805
health 1.39488 1.36012 1.38864 1.37808
polici 1.18087 1.1593 1.16752 1.15817
impact 1.39323 1.35911 1.3894 1.37858
countri 1.38996 1.35786 1.38996 1.37654
develop 1.37452 1.34745 1.37523 1.36426
nation 1.22695 1.18433 1.21671 1.19852
sustain 1.35513 1.31744 1.35226 1.3439
economi 1.36081 1.31772 1.35133 1.33826
psycholog 1.39601 1.37095 1.39514 1.38225
group 1.38894 1.35866 1.38631 1.37627
peopl 0 1.37228 1.39474 1.3849
social 1.37228 0 1.36679 1.35473
studi 1.39474 1.36679 0 1.38393
research 1.3849 1.35473 1.38393 1.49012e-08
(0.4338699333866362, 0.0024, 1.8506050566886427)
mantel_test_verticle(model_psy_p,model_psy_paragraph,10)
Pairwise-distance
covid pandem health polici impact countri \
covid 0 2.70574 1.67187 0 2.236 0
pandem 2.70574 2.63418e-08 1.22885 0 1.89336 0
health 1.67187 1.22885 0 0 1.0317 0
polici 0 0 0 0 0 0
impact 2.236 1.89336 1.0317 0 0 0
countri 0 0 0 0 0 0
develop 2.236 1.89336 1.0317 0 6.58545e-09 0
nation 0 0 0 0 0 0
sustain 0 0 0 0 0 0
economi 0 0 0 0 0 0
misinfodem 1.35287 4.05861 2.98805 0 3.34632 0
articl 1.13552 2.4577 1.22885 0 1.25066 0
mechan 1.67187 1.22885 0 0 1.0317 0
commun 1.67187 1.22885 0 0 1.0317 0
psycholog 1.70328 2.52328 1.35447 0 0.901913 0
strategi 1.70328 2.52328 1.35447 0 0.901913 0
misinform 2.25478 0.450957 0.841095 0 1.67445 0
inform 1.72034 1.68219 0.558152 0 0.567759 0
develop nation sustain economi misinfodem articl mechan \
covid 2.236 0 0 0 1.35287 1.13552 1.67187
pandem 1.89336 0 0 0 4.05861 2.4577 1.22885
health 1.0317 0 0 0 2.98805 1.22885 0
polici 0 0 0 0 0 0 0
impact 6.58545e-09 0 0 0 3.34632 1.25066 1.0317
countri 0 0 0 0 0 0 0
develop 0 0 0 0 3.34632 1.25066 1.0317
nation 0 0 0 0 0 0 0
sustain 0 0 0 0 0 0 0
economi 0 0 0 0 0 0 0
misinfodem 3.34632 0 0 0 0 2.09875 2.98805
articl 1.25066 0 0 0 2.09875 0 1.22885
mechan 1.0317 0 0 0 2.98805 1.22885 0
commun 1.0317 0 0 0 2.98805 1.22885 0
psycholog 0.901913 0 0 0 2.58051 0.567759 1.35447
strategi 0.901913 0 0 0 2.58051 0.567759 1.35447
misinform 1.67445 0 0 0 3.60765 2.0571 0.841095
inform 0.567759 0 0 0 2.91809 0.901913 0.558152
commun psycholog strategi misinform inform
covid 1.67187 1.70328 1.70328 2.25478 1.72034
pandem 1.22885 2.52328 2.52328 0.450957 1.68219
health 0 1.35447 1.35447 0.841095 0.558152
polici 0 0 0 0 0
impact 1.0317 0.901913 0.901913 1.67445 0.567759
countri 0 0 0 0 0
develop 1.0317 0.901913 0.901913 1.67445 0.567759
nation 0 0 0 0 0
sustain 0 0 0 0 0
economi 0 0 0 0 0
misinfodem 2.98805 2.58051 2.58051 3.60765 2.91809
articl 1.22885 0.567759 0.567759 2.0571 0.901913
mechan 0 1.35447 1.35447 0.841095 0.558152
commun 0 1.35447 1.35447 0.841095 0.558152
psycholog 1.35447 0 0 2.18461 0.841095
strategi 1.35447 0 0 2.18461 0.841095
misinform 0.841095 2.18461 2.18461 0 1.35447
inform 0.558152 0.841095 0.841095 1.35447 0
Pairwise-distance
covid pandem health polici impact countri \
covid 0 1.40424 1.39832 1.18101 1.3943 1.39452
pandem 1.40424 0 1.39659 1.17943 1.39343 1.39162
health 1.39832 1.39659 2.98023e-08 1.16945 1.38795 1.38601
polici 1.18101 1.17943 1.16945 0 1.17588 1.17229
impact 1.3943 1.39343 1.38795 1.17588 0 1.38641
countri 1.39452 1.39162 1.38601 1.17229 1.38641 2.98023e-08
develop 1.38326 1.38447 1.37717 1.15938 1.37587 1.36229
nation 1.22899 1.22584 1.22499 0.837969 1.22654 1.23319
sustain 1.35901 1.3518 1.34372 1.13024 1.34354 1.35344
economi 1.36338 1.36026 1.35577 1.13045 1.3521 1.35641
misinfodem 1.01939 1.01529 1.02419 0.688077 1.01745 1.01177
articl 1.3135 1.30856 1.30736 1.07248 1.30274 1.30487
mechan 0.99498 0.993759 0.985503 0.638435 0.985301 0.979537
commun 1.37027 1.36762 1.35845 1.15846 1.35994 1.35824
psycholog 1.40124 1.39922 1.39094 1.17297 1.39029 1.38934
strategi 1.03041 1.02139 1.0143 0.697534 1.0183 0.999283
misinform 1.00806 1.00548 0.989343 0.65129 0.995768 0.991685
inform 1.37625 1.37323 1.36722 1.14505 1.36564 1.364
develop nation sustain economi misinfodem articl \
covid 1.38326 1.22899 1.35901 1.36338 1.01939 1.3135
pandem 1.38447 1.22584 1.3518 1.36026 1.01529 1.30856
health 1.37717 1.22499 1.34372 1.35577 1.02419 1.30736
polici 1.15938 0.837969 1.13024 1.13045 0.688077 1.07248
impact 1.37587 1.22654 1.34354 1.3521 1.01745 1.30274
countri 1.36229 1.23319 1.35344 1.35641 1.01177 1.30487
develop 0 1.15853 1.30987 1.32839 1.00215 1.29744
nation 1.15853 2.10734e-08 1.18406 1.23581 0.733643 1.10257
sustain 1.30987 1.18406 0 1.32734 0.952904 1.24464
economi 1.32839 1.23581 1.32734 0 0.956511 1.24344
misinfodem 1.00215 0.733643 0.952904 0.956511 7.45058e-09 0.87454
articl 1.29744 1.10257 1.24464 1.24344 0.87454 0
mechan 0.963167 0.722768 0.92784 0.930869 0.216625 0.862229
commun 1.34383 1.18786 1.31982 1.32736 0.991352 1.26474
psycholog 1.37953 1.22554 1.33487 1.34889 1.02037 1.32411
strategi 0.940785 0.755852 0.962041 0.978575 0.264792 0.885626
misinform 0.976789 0.742552 0.945968 0.951166 0.210444 0.883107
inform 1.34691 1.21359 1.32984 1.33552 0.908467 1.28782
mechan commun psycholog strategi misinform \
covid 0.99498 1.37027 1.40124 1.03041 1.00806
pandem 0.993759 1.36762 1.39922 1.02139 1.00548
health 0.985503 1.35845 1.39094 1.0143 0.989343
polici 0.638435 1.15846 1.17297 0.697534 0.65129
impact 0.985301 1.35994 1.39029 1.0183 0.995768
countri 0.979537 1.35824 1.38934 0.999283 0.991685
develop 0.963167 1.34383 1.37953 0.940785 0.976789
nation 0.722768 1.18786 1.22554 0.755852 0.742552
sustain 0.92784 1.31982 1.33487 0.962041 0.945968
economi 0.930869 1.32736 1.34889 0.978575 0.951166
misinfodem 0.216625 0.991352 1.02037 0.264792 0.210444
articl 0.862229 1.26474 1.32411 0.885626 0.883107
mechan 0 0.948595 0.988374 0.243203 0.142187
commun 0.948595 1.49012e-08 1.36425 0.96571 0.939483
psycholog 0.988374 1.36425 2.58096e-08 1.02155 0.999707
strategi 0.243203 0.96571 1.02155 3.72529e-09 0.232545
misinform 0.142187 0.939483 0.999707 0.232545 0
inform 0.922925 1.33091 1.37231 0.984985 0.948071
inform
covid 1.37625
pandem 1.37323
health 1.36722
polici 1.14505
impact 1.36564
countri 1.364
develop 1.34691
nation 1.21359
sustain 1.32984
economi 1.33552
misinfodem 0.908467
articl 1.28782
mechan 0.922925
commun 1.33091
psycholog 1.37231
strategi 0.984985
misinform 0.948071
inform 0
(-0.22712002342760387, 0.8713, -1.1712869238069015)